Real Estate Analysis Zillow Data-Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real-Estate-Analysis\n",
    "\n",
    "Analysis of Real Estate Trend in USA in last 10 years from Zillow.com.  Among the factors investigated were the percentage median sale price increases and pattern in the seasonality of number of houses sold during the year.\n",
    "\n",
    "[Plot1 : Median Sale Price growth over years in U.S](#median)<br/>\n",
    "[Plot2 : Top 10 states by percentage increase in Median Sale Price from Sept 2011](#plot2)<br/>\n",
    "[Plot3 : Top 10 metros by size](#plot3)<br/>\n",
    "[Plot4 : Median Sale Price increase for Cities in Bay area ](#plot4)<br/>\n",
    "[Plot5 : Home Sale Counts by state - US Map](#plot5)<br/>\n",
    "[Plot6 : Days On Market  - Seasonality Trend over the years](#plot6)<br/>\n",
    "[Plot7 :Days On Market trend - Heat Map](#plot7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as tick\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from plotly import __version__\n",
    "from plotly.offline import init_notebook_mode,iplot, plot\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "plotly.tools.set_credentials_file(username = 'singuava', api_key = 'Hd6ccbecmOGgaZbsHva2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Format function to show numbers morethan 1000 in the ticks in K\n",
    "def fmt_x(x,y):\n",
    "    if x >= 1000000:        \n",
    "        val = int(x) / 1000000\n",
    "        return '{:.1f}M'.format(val)\n",
    "    elif x >= 1000:        \n",
    "        val = int(x) / 1000\n",
    "        return '{val:d}K'.format(val=int(val))\n",
    "    else:\n",
    "        return int(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2008-03</th>\n",
       "      <th>2008-04</th>\n",
       "      <th>2008-05</th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-07</th>\n",
       "      <th>2008-08</th>\n",
       "      <th>2008-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102001</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>188300.0</td>\n",
       "      <td>184800.0</td>\n",
       "      <td>181000.0</td>\n",
       "      <td>177700.0</td>\n",
       "      <td>175800.0</td>\n",
       "      <td>174700.0</td>\n",
       "      <td>173700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>224700.0</td>\n",
       "      <td>226600.0</td>\n",
       "      <td>230600.0</td>\n",
       "      <td>233800.0</td>\n",
       "      <td>236400.0</td>\n",
       "      <td>234900.0</td>\n",
       "      <td>230800.0</td>\n",
       "      <td>227700.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>394913</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>373800.0</td>\n",
       "      <td>380000.0</td>\n",
       "      <td>384100.0</td>\n",
       "      <td>385900.0</td>\n",
       "      <td>387100.0</td>\n",
       "      <td>389400.0</td>\n",
       "      <td>396500.0</td>\n",
       "      <td>399100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>753899</td>\n",
       "      <td>Los Angeles-Long Beach-Anaheim, CA</td>\n",
       "      <td>2</td>\n",
       "      <td>472800.0</td>\n",
       "      <td>461900.0</td>\n",
       "      <td>445100.0</td>\n",
       "      <td>435000.0</td>\n",
       "      <td>421000.0</td>\n",
       "      <td>408500.0</td>\n",
       "      <td>392400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>602300.0</td>\n",
       "      <td>612300.0</td>\n",
       "      <td>614300.0</td>\n",
       "      <td>618400.0</td>\n",
       "      <td>623700.0</td>\n",
       "      <td>627400.0</td>\n",
       "      <td>626700.0</td>\n",
       "      <td>621900.0</td>\n",
       "      <td>623500.0</td>\n",
       "      <td>626800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID                          RegionName  SizeRank   2008-03   2008-04  \\\n",
       "0    102001                       United States         0  188300.0  184800.0   \n",
       "1    394913                        New York, NY         1       NaN       NaN   \n",
       "2    753899  Los Angeles-Long Beach-Anaheim, CA         2  472800.0  461900.0   \n",
       "\n",
       "    2008-05   2008-06   2008-07   2008-08   2008-09    ...      2017-10  \\\n",
       "0  181000.0  177700.0  175800.0  174700.0  173700.0    ...     221000.0   \n",
       "1       NaN       NaN       NaN       NaN       NaN    ...     373800.0   \n",
       "2  445100.0  435000.0  421000.0  408500.0  392400.0    ...     602300.0   \n",
       "\n",
       "    2017-11   2017-12   2018-01   2018-02   2018-03   2018-04   2018-05  \\\n",
       "0  224700.0  226600.0  230600.0  233800.0  236400.0  234900.0  230800.0   \n",
       "1  380000.0  384100.0  385900.0  387100.0  389400.0  396500.0  399100.0   \n",
       "2  612300.0  614300.0  618400.0  623700.0  627400.0  626700.0  621900.0   \n",
       "\n",
       "    2018-06   2018-07  \n",
       "0  227700.0       NaN  \n",
       "1       NaN       NaN  \n",
       "2  623500.0  626800.0  \n",
       "\n",
       "[3 rows x 128 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SalePrice = pd.read_csv(\"Median Price\\Sale_Prices_msa.CSV\")\n",
    "df_SalePrice.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Median Sale Price growth over years in U.S <a class=\"anchor\" id=\"median\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2008-03</th>\n",
       "      <th>2008-04</th>\n",
       "      <th>2008-05</th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-07</th>\n",
       "      <th>2008-08</th>\n",
       "      <th>2008-09</th>\n",
       "      <th>2008-10</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>188300.0</td>\n",
       "      <td>184800.0</td>\n",
       "      <td>181000.0</td>\n",
       "      <td>177700.0</td>\n",
       "      <td>175800.0</td>\n",
       "      <td>174700.0</td>\n",
       "      <td>173700.0</td>\n",
       "      <td>172700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>224700.0</td>\n",
       "      <td>226600.0</td>\n",
       "      <td>230600.0</td>\n",
       "      <td>233800.0</td>\n",
       "      <td>236400.0</td>\n",
       "      <td>234900.0</td>\n",
       "      <td>230800.0</td>\n",
       "      <td>227700.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RegionName  SizeRank   2008-03   2008-04   2008-05   2008-06   2008-07  \\\n",
       "0  United States         0  188300.0  184800.0  181000.0  177700.0  175800.0   \n",
       "\n",
       "    2008-08   2008-09   2008-10   ...      2017-10   2017-11   2017-12  \\\n",
       "0  174700.0  173700.0  172700.0   ...     221000.0  224700.0  226600.0   \n",
       "\n",
       "    2018-01   2018-02   2018-03   2018-04   2018-05   2018-06  2018-07  \n",
       "0  230600.0  233800.0  236400.0  234900.0  230800.0  227700.0      NaN  \n",
       "\n",
       "[1 rows x 127 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select USA\n",
    "df_US = df_SalePrice.iloc[0:2:2,1::1]\n",
    "df_US"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>Median_Sales_Price</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-03</td>\n",
       "      <td>188300.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-04</td>\n",
       "      <td>184800.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-05</td>\n",
       "      <td>181000.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearMonth  Median_Sales_Price  Year\n",
       "0   2008-03            188300.0  2008\n",
       "1   2008-04            184800.0  2008\n",
       "2   2008-05            181000.0  2008"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_US.drop(['SizeRank','RegionName'], axis=1, inplace = True)\n",
    "\n",
    "#transposing into columns\n",
    "df_US = df_US.transpose()\n",
    "\n",
    "#Reset index\n",
    "df_US.reset_index(level=0,inplace=True)\n",
    "\n",
    "##Name columns\n",
    "df_US.columns = ['YearMonth','Median_Sales_Price']\n",
    "\n",
    "##Make a new column Year from YearMonth\n",
    "df_US['Year']= (df_US['YearMonth'].str.split('-').str[0])\n",
    "df_US.head()\n",
    "\n",
    "df_US.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Median_Sales_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>176660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>157666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>160258.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>152166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>157133.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Median_Sales_Price\n",
       "0  2008       176660.000000\n",
       "1  2009       157666.666667\n",
       "2  2010       160258.333333\n",
       "3  2011       152166.666667\n",
       "4  2012       157133.333333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Make a new dataframe groupby year\n",
    "df_US = df_US.groupby('Year', as_index=False).mean()\n",
    "df_US.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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EEClXNXicjawz3dBa0lVbDtgtd2zcDHw7N29h4gLq/LI0SxJV7ZwY03sHYI7c\n/GWI9qIv1/m9z5n7Xl8E1k7Tjejp+m2iJkqlz1J4jBcs99e03ASis7EeafrqxEWlp+Nkvgrpb8tt\ny4FvlM0/oWz+1hXWk43l/FL+WCpbZsNKx2q9x0FBuqWJ80b2W1kuTZ8j5W0yVcbmTcsuTmns92fS\n87lVlp83HTNO9L2xH9AnN3+xtO1nmHm84JG0cL6p8/OvUvY93d7C8keU/VZWys2bk6jRcRFR1T2f\nLhu7fCJxjsiOt2+nY3Fy7nvYsCztKVQ4p+eW+RHRrnhPZjwnzQucmPt+tqxz//wrpdu9wvzlKP0n\njaLO8Y6JNu0TU/p7gMXT9IFE7TMnDfNZIf1NlJqSbJmm9SDayX+e5v26IN2SuWP7zfSdzJk7/v6Q\n+55/0xbHWh37ZAHifOBp/w+sM31vSv+5Y4EV0vR5iCEys8+1eYX0fYgac9nj0bT8eWXT5ypL15e4\nSenEf8NQ0u86fabTif9qB0ZUyf8f0zI/bWDfjaTg/MAsPIemtHvm9uu1pPNomtePuLmb/b7vaWH7\nM/1/Ab8jru0uIWr1zZObNx/RB8K7Ke0kok+Kdjtm9WibR9MzoIceHfXBrAnGewAX5068TpSQZb2X\nZ9MeKkg7hNJFhqcTdHZRMSb94RVeuFU50Q8uW+ek3PsPiQsVp32D8YPL9o8T1do+KZv2BbBfQfod\ncn/82f79Mr1+g7h5UfjnXO0zET0d31GWh48pXVBmj6saPM5G1pluaC3pWloOOKZsf31adkzMlI4o\nwXg7t8xU4qJ4MjPuiyF1fqZBlALu8ry8lPJaeFFT6RgvWG4gUVUwfxzlj60JpBsBFdIfmVv2PwXz\n18nNnwbMW2E981O6qTGNuKB6PX9cMusvJLehNMqAp30/Jff+fmDuFtbxSNl3/t0Wll+GaK+a30cf\nlh1zTtlvmzYOxtM6n8ptr/CmSdnyP2fG8/RnlPr0yKa9VpZmAPBKbv4USueMr9L39zqNB+NHle23\nScz4G3Lg9w3sm5NS2msrzL+y7Dczrsrj2Arr2Cztw/z5NNu/U4kq75Xy15eolp3/LvLnn7uAnhXS\n7la27DRKAVM+/extdazVuc+dUlXtSo/zKqzjO+Ruoqf1ZMfndKoEupT+i1p6nFKQdgilAoHsUf6f\n/QTReWPRtudMy08Hlm5g342k4PzALD6HpvQ/Y8bzwqT0m8hPexSYv4Xtz/T/Rem6Lv+YWPa7yc7d\n32vP41VKSNX1AAAgAElEQVSPtnuomrpIO3L3ae5+KFGKch0RHM5O3NF+kwj29qPU9jKfdhTRC+lN\nxF3oOYiLuFOIKtu1VpfPr/MJoi3TncTJvBdRYvV7IjD6e+XUs4a7/54Y1/hnRMnRGylfvYmL9seJ\n0qbl3f3qgvR3EPvjPiKY65XW8Tti/71VnqbGfH3m7jsQQ1fdTgSjvYnv7xWiiuDOwKGNrL9Z3P1s\nYr9cRRxPvYgL1X8QJSJHF6R5iijZO564EfQpURL3BXGBfCZRY6GuIZXc/TniYvIq4oKzV3o+m2iT\nl1Vd/LhwBbVtYzxxzP845XUq8R2+TPSivKK7VxvLelSF15mnKPX0/w93L8yrxwgMGxHH0njiJsES\n1NehXqu4+11E9enLie9+LiLvjxA9c3/PZ243X+763Ov/evG40vltvkIcb4cSJfITiMDqK+KYu4C4\nsK+1Y8zWyKqMvkuca6py99OI4/My4ngx4iZdlv4Q4gZnPs0EYlSKSyide74gzrlDfMae1xsxgqhN\ncBPRFnsqUcL5LtGp2nbufnAD672K+L1tV6HH8/z1Y38q9yS+IBWG8nT3+4j/mauIfdObKG2/GVjL\nK/fMjUfV9XWAn1Kq0fIl8f9wMNGBZ+HY7O5+E1Ez4iLixtBk4v/0PeJ73DOlb+/2t/l92pfq+7Sw\n6rK7/x1YCTif6BtiDuJ/8y/AZu5+RlG61krn+hWJc/9zxH9C9p/9EFFCvJ67T6ywiq2JEvyR7v7f\nWZHHWcXdf01cs1xOqb+FPsTx9Bdi1IYhXtuoO+VOJK4XT6PUtGxO4r9xPNGPyc+Ab7n7Pa34GNJE\n5nHnRUREpEMzs2uJmg2/9BjSRaRhZnYfUfXzTHf/abPz09GY2Z+BrYi24JX6DRBpNTO7jWhKtqe7\n39DS8iJdiYJxERHp8Mzsm8DzRGnLeu7e0YYLk07EzJYh+sWAKFV6pZn56YjMbA3gSeAJd1+7peVF\nGpF+iy8Spcore+fovE2kzaiauoiIdAhmtp2Z/cbMVsyG/zOzOcxsO+BBIhB/XIG4tEbq5f8Copr5\nnxWIF3P3p4me5Ncys448zKV0bicQ/en8TIG4dEc9m50BERGRZCBxYXYCMN3MPibaTmb/VVkHfCJ1\nM7OjiA7PFiLa0k4m2hxLZT8l2qLXM0yhSE3SMHb/BY5z9zubnR+RZlA19W7CzJ4FliJ6eVQpgIh0\nRHMSQ6rNS6mTmulEh1cfEJ3XFHbKJFKDJYkO8qYT/4WvUhoHXEREuodliBuMr7n7qs3OjILxbiKV\nMBX2vikiIiIiItKNTHT3eZudCVVT7z4mAf369evHoEGDmp0XERERERGRdvXcc88xceJEiNio6RSM\ndx+vAIsMGjSIkSNHNjsvIiIiIiIi7WrDDTdk1KhR0EGa7ao3dREREREREZF2pmBcREREREREpJ0p\nGBcRERERERFpZwrGRURERERERNqZgnERERERERGRdqZgXERERERERKSdKRgXERERERERaWcKxkVE\nRERERETamYJxERERERERkXamYFxERERERESknSkYFxEREREREWlnCsZFRERERERE2pmCcRERERER\nEZF2pmBcREREREREpJ0pGBcRERERERFpZwrGRUREREREpMv74otm52BGCsZFRERERESkS/vrX2Hs\n2GbnYkYKxkVERERERKRLcofTT4ettoJp05qdmxn1bHYGRERERERERNrapEmw//5w663NzkkxBeMi\nIiIiIiLSpfz3v7D99vD886Vp/frBxInNy1M5VVMXERERERGRLuNvf4M11pgxED/iCPjOd5qXpyIK\nxkVERERERKTTc4czzoAtt4SPP45pc8wBV10F558PZs3NX7kuGYybWS8z28TMhpnZ42b2rplNMbO3\nzexWM9uwQrojzOxmM/u3mX1oZlPNbLyZ3W9me5tV/vrMbDYzO8zMnjazSWY20cxGm9keVdK8bmZe\nJT8rp7x7yvfs9e4LERERERGRru6zz2D33eGEEyIoB1hkERg9GoYObWrWKuqqbcaHAPel1+OAscBn\nwArATsBOZnaqu59Ulu54YAHgeWBMSrMEsDGwCbCzme3o7tPzicysB3A7sC3wCXAvMEdKM8LM1nb3\nI+v5AGY2GLgbGABcBRzo7h2s/z8REREREZHmevXVaB/+z3+Wpq2/PtxyCyy4YPPy1ZIuWTIOTAdu\nAzZw94XdfWt3383dVwZ2B6YBvzCzjcrS7Q70d/fV3H0bd9/d3dcGVgbeA7YD9ivY3lFEIP4C8C13\n39Hdt8qlO8LMtqs182a2MXA/EYifBxygQFxERERERGRG994b7cPzgfhhh8H993fsQBy6aDDu7g+6\n+87uPrpg3k3A8PR277J5j7j7ZwVp/gVclN5ulp+XSsV/kt4e4u7v5dK9TJS2A/yslryb2bbAX4A+\nwC/d/Sj3rKKFiIiIiIiIuMNZZ8EWW8BHH8W02WeHK66ACy+M1x1dlwzGa/Bsel60jjRfpefJZdPX\nJqq2v+XuDxekuwWYCqxpZotU24CZ7UWU6M8BHOXup9SRPxERERERkS7vs89gjz3g+ONhempA/I1v\nwMMPw//9X3PzVo/uGowvm57frWVhM1sK+GF6e1fZ7FXT81NFad39c+Bf6e2gKts4BLgGMKJa+nm1\n5E1ERERERKS7eO01WGcduOmm0rR114WxY2Hw4OblqxFdtQO3isxsIWBoentbhWX2JzqB60WUnq9D\n3Lg43d3vKFt8qfT8RpXNvkkE4ktVmH8U0R59CrCHu99e/VN8nc+hlD5LSyreCBAREREREeno7r8f\ndtsNJkwoTTvkEDj33M5RLb1ctwrGzawncB3QD3jA3ctLuTPrMmNHbV8BvwDOLli2T3qeqa15zqT0\nPE+F+VnnbufWGognSxI3DURERERERLokdxg2bMZq6bPPDhdfDAcc0Ny8tUa3CsaBS4nhxv5HWedt\nee7+A+AHZtabKM3eHzgF2NXMtnT3d3KLZ2OPt6aTtYeBDYAfm9lYd7+5xnSvA6NqXHYQcRNCRERE\nRESkU/j8c/jBD+CGG0rTvvENuO02WGut5uWrLXSbYNzMzgMOIMYd38Tdx7WUxt2/IIYrO87MxgG/\nAy4Edswt9ml67kNl2bxPK8w/Gdga+DExLjm1BOTuPpxSz/BVmdlIVIouIiIiIiKdxOuvww47wHPP\nlaatsw7ceissvHDTstVmukUHbmY2DDgSGE8E4i83sJqr0vM2ZtYrN/319LxElbSLlS07E3c/FhgG\n9ACuN7NdG8ijiIiIiIhIp/fAAzF+eD4QP/hgeOihrhGIQzcIxs3sLOAY4ENgM3d/ocFVfUy0He8J\nDMhNfyY9r1lh+3MBK6W3zxYtk8kF5D2JgHyXBvMqIiIiIiLS6bjD2WfD5pvDhx/GtF694LLL4NJL\nO2dHbZV06WrqZnYGcBzwERGI/70Vq9uA2F8fAx/kpj8GvA8samYbFIw1vgvRK/tT7v52Sxtx92PN\nDGassn5LK/ItIiIiIiLS4X3+ORx0EFx/fWnawgtH+/C1125evmaVLlsybmanAscTwfNm7l61VNrM\n1jezvcxsjoJ56wJXpLdXuPu0bF56/dv09hIzWyCXblngjPT217XmvayEfISZ7VxrWhERERERkc7m\njTdivPB8IL722vD0010zEIcuWjJuZtsCP09vXwGOSKXN5V509yxYXppoF36hmT1DdPQ2T5q+Qlrm\nL8QQZ+XOIUrOtwFeNrMHiNLwTYE5gQvc/Y/1fIayEvIbUgn5rfWsQ0REREREpKN76CHYdVf4IFf/\n+MAD4YILYI6Zikq7ji4ZjDNjm+410qPIKEol16OAU4H1gW8B6xDDlo0DbgOuc/c7i1bi7tPMbHvg\nUGIYtO8B04CxwMXuPqKRD6GAXEREREREuip3OO88OPZYmJbqHvfqFUH4wQc3N2/toUsG4/UM+ZVL\n8xpwUiu2OZ0Y9uzCOtIsWcMyxwLHNpovERERERGRjuaLL6J9+HXXlaYttFAMW7buus3LV3vqksG4\niIiIiIiIdExvvhnjhz/zTGna4MHRUdsiizQvX+2ty3bgJiIiIiIiIh3LyJGw+uozBuIHHACjRnWv\nQBwUjIuIiIiIiMgs5g7nnw+bblrqqK1nT7j4Yrj88q7dUVslqqYuIiIiIiIis8wXX8APfwjXXFOa\ntuCC0T58vfWal69mUzAuIiIiIiIis8T//hftw8eOLU377nejffiiizYvXx2BqqmLiIiIiIhImxs1\nKtqH5wPx/feP6d09EAcF4yIiIiIiItKG3OHCC6N9+PjxMa1nz5h2xRUw55zNzV9HoWrqIiIiIiIi\n0iYmT4ZDDoHhw0vTFlgAbrkFNtigadnqkBSMi4iIiIiISKu99RbsuCM89VRp2hprwO23w2KLNS9f\nHZWqqYuIiIiIiEirjB4d7cPzgfjQoTFdgXgxBeMiIiIiIiLSEHe46CLYeGN4//2Y1qMHXHABXHml\n2odXo2rqIiIiIiIiUrfJk+GwwyLozgwcGO3DhwxpXr46CwXjIiIiIiIiUpe334724U8+WZq2+urR\nPnzxxZuXr85E1dRFRERERESkZo88EoF3PhDfd99oH65AvHYKxkVERERERKRF7nDJJbDRRvDeezGt\nRw8477wYyqx376Zmr9NRNXURERERERGp6ssvo334FVeUps0/f7QP33DDpmWrU1MwLiIiIiIiIhW9\n/TbstBM88URp2mqrRfvwJZZoXr46O1VTFxERERERkUKPPgprrDFjIL733tFuXIF46ygYFxERERER\nkZn8/vfRPnzcuHjfoweccw5cc43ah7cFVVMXERERERGRr335JRxxBFx+eWnafPPBzTfDxhs3L19d\njYJxERERERERAeCdd2DnneGxx0rTBg2CO+6AJZdsWra6JFVTFxERERERER57LNqH5wPxPfeMduMK\nxNuegnEREREREZFu7vLLYcgQePfdeD/bbDBsGFx3Hcw1V3Pz1lWpmrqIiIiIiEg3NWUKHHlkdNaW\nGTAAbroJNt20efnqDhSMi4iIiIiIdEPvvhvtw8eMKU37zneiffhSSzUvX92FqqmLiIiIiIh0M48/\nHu3D84H47rvHewXi7UPBuIiIiIiISDdyxRXRPvydd+L9bLPBb38LI0aofXh7UjV1ERERERGRbmDK\nFDjqKLjkktK0AQPgxhths82al6/uSsG4iIiIiIhIFzduHOyyCzzySGnaKqtE+/BvfrN5+erOVE1d\nRERERESkC3vyyWgfng/Ed9st2ocrEG8eBeMiIiIiIiJd1JVXwvrrw9tvx/vZZoMzz4QbboC5525u\n3ro7VVMXERERERHpYqZOhaOPhosuKk3r3z/ah2++efPyJSVdsmTczHqZ2SZmNszMHjezd81sipm9\nbWa3mtmGbZGmYB17mtloM5toZpPM7GkzO8zMCvezmY00MzezoRXmL2pm/07LjDazfnXuChERERER\n6Wbeew822WTGQHzlleGppxSIdyRdMhgHhgD3A8cASwBjgTuACcBOwENm9qs2SPM1M7sIuB5YAxgN\n3Ad8C7gQuNXMetTzAcxsGeAR4NvAPcD33H1iPesQEREREZHu5amnon346NGlabvsEu3Dl166efmS\nmXXVYHw6cBuwgbsv7O5bu/tu7r4ysDswDfiFmW3UyjQAmNlOwKHAOGCVlHYHYFng38AOwOG1Zt7M\nViYC+iWAW4Ft3f3zeneCiIiIiIh0H8OHR/vwt96K92Zwxhlw003Qp09TsyYFumQw7u4PuvvO7j66\nYN5NwPD0du/WpMk5IT0f7+4v59K9BxyS3v60UnX1PDMbDIwCFgKuAHZz9yktpRMRERERke5p6lQ4\n8kjYf3/48suYNu+8cPfdcPzxEZRLx9Mlg/EaPJueF21tGjNbFFgdmALcUp7I3UcBbxPB9VrVNmBm\nGxNV5fsDZ7v7D9x9eh15FBERERGRbuT992HTTeGCC0rTVlwxqqt///vNy5e0rLsG48um53fbIM2q\n6flf7v5FhbRPlS07EzPbFrgb6AOc7O4/riNvIiIiIiLSzYwdG+3DH364NG2nneDxx2GZZZqXL6lN\nQ0ObmdkCwHrAYGBhYH6gN/Ah8AHwItHm+dmOVrJrZgsBQ9Pb29ogzVLp+Y0qq3izbNlyOwPfA3oA\nR7r7BRWWK8/X0Fy+WjKoxuVERERERKSDu+YaOOigUrV0MzjtNDjhBFVL7yxqDsbNbADRXnp/YJX8\nrLJFPff6MzO7FbjS3R9pOJdtxMx6AtcB/YAH3P2uNkiTdYXwWZXVTErP81SYv1V6vrnWQDxZkugF\nXkREREREuoGpU+G44+C880rT+vWDG26ALbZoXr6kfi0G42b2DeBkYF9gdkrB95vA34mS8I+AyURb\n5/5EkDiICFSHAvuZ2YvAKe4+U7vqdnQpsAnwP4o7YmskTbY/vGBerR4GNgB2NbMn3X1YjeleJzp7\nq8Ug4oaCiIiIiIh0QuPHw667wsiRpWkrrAB33gnLLlsxmXRQVYNxMzuTGJKrN1HyeztwM/Cou3/Q\nQtoewMrA1sTQYCsAN5rZT4AfuvvY1me/dmZ2HnAAMfzYJu4+ro3SfJqeqw0WkM37tML8q4ghzM4H\nfmdm7u5nt5Q/dx9OqZf3qsxsJCpFFxERERHplJ55BnbYAd58szRtxx1jOLN5KtW/lQ6tpQ7cjiN6\nAv8BsIC77+Xuf2wpEAdw92nu/py7n+buKxFV268DvkOpWna7MLNhwJHAeCKofrmFJPWkeT09L1Fl\ndYuVLTuTVD39yPR2mJkd01IeRURERESk67vuOlh33VIgnrUPv+UWBeKdWUvV1PcGbmyLTtjc/Xmi\nuvrJwCKtXV+tzOws4Biic7nN3P2FNk6TDXm2opn1rtCj+pplyxZy9wssels4nwjI3d3PaSm/IiIi\nIiLS9Xz1VbQPP/fc0rS+fWHECNiqXYs3ZVaoWjLu7iPaujd0d3/d3R9ty3VWYmZnEKX7HxFB9d/b\nOo27/w94hmhPv0vB+oYQY5OPAx5raftlJeRnm9nRLaUREREREZGu5Z13YvzwfCC+/PIxfrgC8a6h\ny44zbmanAscDHxNBddVS6UbTJKen5zPN7OsR/dIQcBent2fUemOjICA/qsZ8iIiIiIhIJ3f//TBo\nEIzKddW8/fYxfvi3vtW8fEnbamic8Y7OzLYFfp7evgIcYcWD7b3o7mc0mibj7rea2SXAIcA/zex+\nYCrRC3tf4E7gwno+Q1mV9XPMDHc/t4VkIiIiIiLSSU2bBqeeCr/6FXgaq2m22eCXv4QTT4zX0nW0\naTBuZv2AbYhq2e8Df3P3d9pyGzUakHu9RnoUGQVkgXUjab7m7oea2SPAYUSv5T2AF4ErgUsaqe6v\ngFxEREREpHsYNw722gsefLA0bcEFY/zwjTZqXr5k1qk5GDezZYlA+1N3v7xg/nrAHcwY1E4xs2Pc\n/ZJW57QO9Qz51Zo0BesYAYyoY/kNa1jmAuCCVmRLREREREQ6sIcegj33jIA8s9FG0VHbQgs1L18y\na9VT0WF/4LfA6uUzzKwvcBswH1EifgfwL2AO4AIz26D1WRUREREREek6pk+PIco23bQUiJvBSSfB\nffcpEO/q6qmmngXUVxbMOxAYCPwXGOzuEwDM7HLgAOBo4OFW5FNERERERKTLGD8e9t4b7r23NG3g\nQLj+ethss+blS9pPPSXjiwNfAWML5m0POPCbLBBPjktp1mo4hyIiIiIiIl3I6NHRW3o+EN9gA3ju\nOQXi3UnVknEz2zf3dkHgS2Cvgl7Gs6rrA8vSALwHLFg2/Tl3/0cD+RUREREREemUpk+Hs86Cn/88\nek7PnHBC9KDes0uOdSWVtPR175973QuYVjYNoD8wJ/ApsEXBOuYiehbPp7sKUDAuIiIiIiLdwocf\nwr77wt13l6bNNx9cey1sURRFSZdXNRh396870TeziUBvYEt3/yI3/TCit++/uftu5esws0eB7+TX\nJSIiIiIi0l2MGQO77QZvvVWats46cOONsNhizcuXNFc9bcZfJEq4DyibPpRoL35veYJkWeDtunMm\nIiIiIiLSibnDsGEwZMiMgfhxx8HIkQrEu7t6WiXcAKwJnJ3GHH8J2JZoLz4BuKk8gZl9B5gfeKD1\nWRUREREREekcPvoIhg6FP/2pNK1/f7j6athmm6ZlSzqQeoLxC4AtgU2Bw9M0I9qRH+zukwrS/JAo\nNf9LazIpIiIiIiLSWTz5JOy6K7zxRmna4MFw002wxBLNy5d0LDVXU3f3aUQHbfsDtwD3A5cBa7n7\nbeXLm1k/4JvAg8BM80VERERERLoSdzjvPFhvvRkD8aOOgocfViAuM6qr8/wUkF+dHi0tOxH4XoP5\nEhERERER6TQ+/hgOOABuv700rV8/uOoq2GGH5uVLOi6NZCciIiIiItIKzzwDu+wCr75amrb66nDz\nzfDNbzYvX9Kx1dObuoiIiIiIiCTucPHFsPbaMwbihx8Ojz6qQFyqU8m4iIiIiIhInT75BA48MEq/\nM/PMA1dcEaXkIi1RMC4iIiIiIlKH556L3tJffrk0bdAguOUWWGaZ5uVLOhdVUxcREREREamBO1x2\nGay11oyB+A9/CI89pkBc6qOScRERERERkRZMmgQHHwwjRpSm9ekTwfkeezQvX9J5KRgXERERERGp\n4p//jHbgL71UmrbyylEtfbnlmpcv6dxUTV1ERERERKSCq66CwYNnDMQPOAAef1yBuLSOSsZFRERE\nRETKfPYZHHYYXH11adpcc8Gll8I++zQvX9J1KBgXERERERHJeeGFqJb+wgulaSusENXSV1ihefmS\nrqXhYNzM5gW2BlYC+gO9qizu7n5Ao9sSERERERFpD9deG72jf/55adp++8FFF8HcczcvX9L1NBSM\nm9mRwOnAnNmkFpI4oGBcREREREQ6pC++gCOOgCuuKE2bc064+GLYf//m5Uu6rrqDcTPbHTg3vR0P\n3AO8DUxuw3yJiIiIiIi0i5deimrp//xnadpyy0W19JVXbl6+pGtrpGT8R+n5FmBfd/+yDfMjIiIi\nIiLSbm64AQ46KMYRz+y5J/z+9zGOuMis0sjQZisR1c4PVyAuIiIiIiKd0eTJcMghEXhngfgcc0QQ\nft11CsRl1mukZPwrYKK7j2/rzIiIiIiIiMxqr7wS1dKfe640bZllolr6oEHNy5d0L42UjD8HzGNm\nfds6MyIiIiIiIrPSrbfCaqvNGIjvuiuMHatAXNpXI8H42UAP4LA2zouIiIiIiMgs8eWX0Vv6LrvA\np5/GtNlnjyHLbrwR+qqoUdpZ3dXU3f0uMzsJ+KWZOXCeu3/R9lkTERERERFpvddei9Lvp58uTVtq\nqaiWvvrqzcuXdG+NDG32YHo5Cfg18AszewH4tEoyd/dNGsifiIiIiIhIw+68E4YOhYkTS9N22AGu\nvBLmnbdp2RJpqAO3Dcve9wZaup/kDWxHRERERESkIVOmwE9/CuecU5rWqxf89rdw5JFg1ry8iUBj\nwfj+bZ6LNmZmvYANgC2BdYElgPmA8cBjwIXuPrIg3XLA94E1gTWAbwEG7OLut9aw3T2BQ4BViHb1\nLwJXAZe4+/SC5UcCQ4D93X14wfxFgfuAbwOPAFu7+8Ty5UREREREpOSNN2C33eCJJ0rTllgCbr4Z\nvvvd5uVLJK+RNuNXz4qMtLEhRBALMA4YC3wGrADsBOxkZqe6+0ll6Q4BftTIBs3sIuBQYDLwADAV\n2AS4ENjEzHZx92l1rG8Z4H7iRsI9wI7u/nkjeRMRERER6S7+/GfYd1/46KPStG22geHDYcCApmVL\nZCaN9KbeGUwHbgM2cPeF3X1rd9/N3VcGdgemEW3dNypL9zzwW2A3YBlgVC0bM7OdiEB8HLBK2t4O\nwLLAv4EdgMNrzbyZrQyMJgLxW4FtFYiLiIiIiFQ2dSr85CcReGeBeM+e8LvfwR//qEBcOp5Gqql3\neO7+IPBghXk3mdlmwAHA3sBDuXl/yC9rtTckOSE9H+/uL+fW956ZHQKMBH5qZhcUVVcv2+Zg4K9A\nf+AK4KCW0oiIiIiIdGdvvRXV0seMKU1bdNGolr722s3Ll0g1VYNxM9sgvfzc3Z8um1YXd3+4kXSz\nyLPpedHWrii1614dmALcUj7f3UeZ2dvAIsBawJjyZXLr2hj4I9AHONvdf9za/ImIiIiIdGV//Svs\nsw98+GFp2hZbwDXXwPzzNy9fIi1pqWR8JNET+ktEe+v8tHp4DdtqT8um53fbYF2rpud/VRlv/Ski\nGF+VCsG4mW0L3AzMAZzs7r9qg7yJiIiIiHRJX30FJ50Ep59emtajB5x2WlRXn62rNsiVLqOlAPlN\nIpB+p2Bap2RmCwFD09vb2mCVS6XnN6os82bZsuV2Br5H9MB+pLtfUMuGzWwopc/SkkE1LiciIiIi\n0qG98w7ssQc8nKt7+41vwI03wvrrNy9fIvWoGoy7+5K1TOsszKwncB3QD3jA3e9qg9X2Sc+fVVlm\nUnqep8L8rdLzzbUG4smSRM/xIiIiIiLdwn33wV57wfjxpWmbbw7XXgsLLNC8fInUqyNVHW8PlxLD\njf2P6LytLWS9vLWmtsDDxLjou5rZk+4+rMZ0r1Njj+9EyXi/BvImIiIiItJ006bBr34Fp54Knq68\nZ5sNfvlLOPFEVUuXzqfbBONmdh7Rg/o4YBN3H9dGq/40Pfepskw279MK868ihjA7H/idmbm7n93S\nht19ODC8lkya2UhUii4iIiIindC4cVEa/mBuvKSFFoIRI2Cj8sGKRTqJbnH/yMyGAUcC44lA/OUW\nktTj9fS8RJVlFitbdiapevqR6e0wMzum1TkTEREREenkHnoIVl11xkB8443h2WcViEvnVjUYN7OH\nzKzNDnEz629mvzKzI1teus22eRZwDPAhsJm7v9DGm8iGSVvRzHpXWGbNsmULFQTkR7dB/kRERERE\nOprwRj0AACAASURBVJ3p06NK+qabRsk4gBmcfDLce2+UjIt0Zi2VjK8P3G9mj5rZfmbWt5GNmNna\nZnY+0eP4z6hepbvNmNkZwHHAR0Qg/ve23oa7/w94Bpgd2KUgD0OI8czHAY/VsL58QH62AnIRERER\n6W7Gj4+xwk86KYJygIED4Z574JRTYggzkc6upTbjKwG/AbYH1gIuNbP7gEeBscA/gA/dfVqWwMz6\nEEN4rUGUCG8BLE50dDYO+Alwedt+jJmZ2anA8cDHRCBetVS6lU4HbgHONLMx7v5KysMCwMVpmTPc\nfXotK3P3C8wMog352akN+bmzIN8iIiIiIh3K6NGw++4xfFlmgw3ghhti+DKRrqKloc1eBHY0s8FE\nVe9tga0pDcUFgJlNAqYQvXXn71NlPY3/hwjAL3H3z9sm65WZ2bbAz9PbV4AjUnBb7kV3PyOXbjVK\nwTPACun5N2Z2bDbR3dfKr8TdbzWzS4BDgH+a2f3AVKLn9r7AncCF9XyGsoD8HDNDAbmIiIiIdFXT\np8NZZ8HPfx49p2dOPDF6TO/Zbbqelu6ipkPa3Z8AdjOzAcBewObA2sCAtEj5+NnTgeeBR4Cb3H10\n22S3ZgNyr9dIjyKjgDNy7/sCgwuWW7alDbr7oWb2CHAY0Wt5D+BF4EriJkRNpeJl61RALiIiIiJd\n3gcfwH77wd13l6bNNx9cdx18//vNy5fIrFTX/SV3nwBckB6Y2TLAwsD8wBzABOAD4GV3rzSM1yxX\nz5BfZelGUirNb2S7I4ARdSy/YQ3LfL2/RURERES6mjFjYLfd4K23StPWXRduvBEWXbR5+RKZ1VpV\n2SO1jX6ljfIiIiIiIiLdhDsMGwYnnABffVWa/pOfwGmnQa9ezcubSHtQywsREREREWlXEybA0KFw\n112laf37wzXXwNZbNy1bIu1KwbiIiIiIiLSbJ56IaulvvFGaNngw3HQTLLFE8/Il0t5aGmdcRERE\nRESk1dzhvPNg/fVnDMSPPhoefliBuHQ/KhkXEREREZFZ6uOP4YAD4PbbS9P69YPhw2H77ZuWLZGm\nUjAuIiIiIiKzzNixsMsu8NprpWlrrAE33wxLLdW8fIk0m6qpi4iIiIhIm3OHiy6CddaZMRA/4gh4\n5BEF4iIqGRcRERERkTb1ySdw4IFR+p3p2xeuuAJ23rl5+RLpSBSMi4iIiIhIm3nuuaiW/sorpWmD\nBsEtt8AyyzQvXyIdjaqpi4iIiIhIq02ZAqefDmutNWMg/sMfwmOPKRAXKddwybiZGbADsBmwGNDb\n3TfJzZ8bWB1wdx/d2oyKiIiIiEjH9OijcPDB8K9/lab16QOXXQZ77NG8fIl0ZA0F42a2LHA7sAJg\nabKXLTYZ+AOwtJmt6e7PNJxLERERERHpcD76CH760wi681ZbDUaMgOWWa06+RDqDuqupm1l/4H5g\nReAfwC+AT8qXc/dpwMVEsL5T67IpIiIiIiIdhTvccAMsv/yMgfjcc8PZZ8MTTygQF2lJI23Gf0xU\nS/8rsKa7/xr4osKyd6XnTRvYjoiIiIiIdDCvvgpbbAF77gnvvVeavs028MILcPTR0FPdRIu0qJFg\nfDuiSvqx7v5VtQXd/b/Al4C6axARERER6cSmToUzzoAVV4R77ilNX2QRuP12+OMfYfHFm5c/kc6m\nkXtWSwFfuPu/a1x+EtCvge2IiIiIiEgHMGZMdND2/POlaWZwxBFw6qkxhriI1KeRYNxrTWdmsxOB\n+ExtykVEREREpGP76CM44QT4/e9nnL7qqjFtzTWbky+RrqCRauqvAbOnHtVbsiURuNdaii7y/+zd\nebxX0/7H8denQUoqLimiqx8yXAmVIUMKyZBSJEM3rqlBuYYM13hNhdBAhJSbRGXIUBnS4KoUrrl7\nTYUoU4M0dz6/P9b++n7P6Qzf8z3DPsP7+Xicxz577bX2/pwe1Pl819qfJSIiIiIxc4fx40OBttRE\nPFGg7Z13lIiLFFUmyfjLhArpV+TXycx2BO4hzKS/kMFzRERERESklCUKtHXvrgJtIiUpk2R8MLAc\nuNDM7jWzXVMvmll9M7sEeB9oAnwPjChypCIiIiIiUmLyKtC2884waZIKtIkUt0J/puXuP5vZqYRt\ny/pHXwCY2c/AdolT4Fegk7v/XgyxioiIiIhICcirQFvfvnDbbSrQJlISMpkZx93fAg4AngI2EhJv\nA7aPjpuBp4GD3f3d4glVRERERESK0/LlcMkl0Lp19kS8eXOYNw+GDlUiLlJSMn7bw92/Ac4xswuA\nFkBDQnK/DFjg7quLJ0QRERERESlO7vD003DZZdnfC69VK2xV1q+f3gsXKWlF/l/M3dcBbxVDLCIi\nIiIiUsK+/hp694apU7O3n3wyDB8OjRvHE5dIZZPRMnURERERESlfNm6EQYNCgbbURDxRoG3yZCXi\nIqWp0Mm4mR1kZtPN7O40+g6J+h6QWXgiIiIiIlJUc+bAQQfBNdfA2rWhzQwuvRQ++wxOOy2ci0jp\nyWRm/K/A0cB7afT9GGgD9MjgOSIiIiIiUgQrVkCvXrkXaJs7VwXaROKUSTJ+THScnkbfF6Nj2wye\nIyIiIiIiGUgUaNt7b3jooXAOoUDb4MEwfz60ahVvjCKVXSYF3HYF1rr7soI6uvtSM1sbjRERERER\nkRKmAm0i5UMmM+PVgaxC9N8M1MrgOSIiIiIikqb8CrRNnKgCbSJlTSbJ+BJgGzNrWlDHqE9t4IcM\nniMiIiIiImmYMwcOPnjLAm19+4YCbV26qECbSFmTSTL+JmDALWn0/Sfg0RgRERERESlGK1aEJemt\nW8NHHyXbDzggFGgbNkwF2kTKqkyS8fsJS89PN7N/mVnDnB3MrKGZjQVOJyxpv79oYYqIiIiISII7\nPPMM7LMPjBiRvUDbPffAggUq0CZS1hU6GXf3hcDlhNnxs4DFZjbfzCZFXwuAxUD3aMhV7v5xHrcr\nEWZW3czamdlgM5trZj+Y2QYzW2JmE82sTQHjzzKz2Wa20sxWm9kCM+tjZvn+eZnZCWb2qpn9amZr\nzOxjM/uHmdXIo/9oM3MzuzmP63XN7K2oz2dm1ijdPwMRERERqZi+/hpOOgm6dYOlS5PtJ50En34K\nV1wB1TIp0ywipSqj/03dfZiZLQXuBXYBDo6+Ui0BrnD3Z4oWYkaOBl6Lvl8KvAv8DuwLdAG6mNmt\n7n5jzoFm9gDQG1gHvAFsBNoBw4F2Zna6u2/OZdwAYBBh1cAMYHkUx23AyWbWzt3XpPsDmNmOwDTg\nQMKe7u3d/ed0x4uIiIhIxbJxI9x3H9x8c/K9cICGDcNy9NNO03vhIuVJxp+ZufsEM3uOkKgeCuxE\nmC1fCswF3nD3TcUSZeFlAZOAIe4+O/WCmXUDngRuMLM33f3NlGtdCIn4UuAod/88at+J8N57Z6Av\nMCTHPVsAA4E1QFt3nxe11wZeBo4Cbgf+nk7wZrYr4cOEpsBs4GR3X1WYPwARERERqTjmzoWLLsr+\nXrgZ9OkDt90GdevGF5uIZKZIC1iiZHta9FVmuPt0YHoe1542s+OAvwHnkL243LXR8epEIh6NWWZm\nvQgz3teY2TB3T93e7RrCBxGDEol4NG61mZ0HfA70NrNb3H1FfrGb2Z6ERLwxMAXo4u5r8xsjIiIi\nIhXTihVw3XXw0EPJ98IhFGgbOVLvhYuUZ5kUcKsI3o+Of7yDHb2PfTCwAZiQc4C7zyQsvW9AWAmQ\nGLcV0CE6fTKXcV8Bc4CtgBPzC8rMmhFmwhsDzwCnKhEXERERqXxUoE2k4qusyfie0TF1//MDo+Mn\n+STA83P0hbCUvBbwq7t/WYhx2ZjZoYSZ952AR4Du7r4xr/4iIiIiUjHlVaDtxBPhk09UoE2kosj3\nf2MzSyz1Xuzu5+VoKwx393YZjCt2ZtYA6BmdTkq5tHt0XJzP8G9y9E39/hvyltu4VEcAVwLbAPe4\n+1X53OsPZtaT5M9SkOZp9hMRERGRGORXoG3oUOjSRQXaRCqSgj5TaxMdF+bSVhhecJeSZ2bVgLFA\nXUKBuRdTLteOjr/nc4vV0XHbYhiXKvFBxTvpJuKRPxMqtouIiIhIOTZ3Llx8MXz4YbLNDHr3httv\nV4E2kYqooGT8vOi4Mpe28ughQuL7LaF4W6rE54yF/eAg03Gp5gAtgFZmNtTd+6U5bhEwM82+zQkf\nQoiIiIhIGbFyJVx77ZYF2po1CwXaDjkkvthEpGTlm4y7+5h02soDMxtCqKC+FGjn7ktzdPktOtYm\nb4lrv6W0ZTou1avAPcB44FIzc3fvn8/9AHD30cDogvoBmNkMNIsuIiIiUia4w8SJ0K9f9vfCa9WC\nW26B/v2hevX44hORklfoAm5m1jH62qEkAioJZjYY6Af8REjEP8+l26Lo2DifW+2ao2/q97sVclw2\n7v4scCawEegXfXggIiIiIhXMokVw8slwxhm5F2i78kol4iKVQSbV1J8HJgLrijmWEmFmdwGXA78A\nx7n7p3l0TWx3tp+Z1cyjT8scfSG8T78W2N7M/i+PcYmNJ97P4zqQa0J+f379RURERKT82LgR7r4b\n9t0XXnkl2d6wIUyYAC+9BH/+c2zhiUgpyyQZ/xVY5e6rC+wZMzMbCFwFLCck4h/k1dfdvwXeI+wH\nfnou9zqasC/5UsI73olxG4Ap0enZuYxrAhxG2L/85YJizpGQ91dCLiIiIlL+zZ0LLVrAgAHJSumJ\nAm2ffQZdu6pSukhlk0ky/glQ18zqFHcwxcnMbgWuBlYQEvF8Z6Ujd0bHQWa2R8q96gMPRqcD3T0r\nx7iBhAJuV5tZq5RxtYFRhD/nB919RTqx55KQ35fOOBEREREpW1auhD594PDDs1dKb9YM5syBBx5Q\npXSRyqqgauq5GQkcCVwK3F684RQPM+sIXB+dfkEoipZb14XuPjBx4u4TzWwE0Av4yMxeJyTE7YA6\nhCX6w3PexN3nm9k1wCDg7Wgv9hWEgmn1gXnAPwrzM7j7s2Z2JqGo22Vmhrv/vTD3EBEREZF4JAq0\n9e8PP/yQbK9ZMxRou+wyvRcuUtkVOhl39yej2d9bzGxr4D53/7X4QyuS7VO+bxF95WYmYVb7D+7e\n28zeAvoQkumqhPfCRwEjcpkVT4y7y8w+BK4gvFu+NfAVMBS4x93XF/aHUEIuIiIiUv4sWhRmw1Pf\nC4dQoO2BB/ReuIgEhU7Go1lfgDXAdYSl2V8QKpVvzmOYu3u7zEIsvMJs+ZXH+HHAuAzGTQWmFqJ/\nT6BnAX2eJbzHLiIiIiJl2MaNMGQI3HQTrFmTbG/QAIYO1XvhIpJdJsvU2+Ryj72jr7x4Bs8RERER\nESkX5s2Diy+GD1LKBZtBr15wxx16L1xEtpRJMn5esUchIiIiIlIOrVwJ//gHPPhgeE88oVkzePhh\nOPTQ+GITkbItk3fGx5REICIiIiIi5YU7TJoE/fqpQJuIZCaTmXERERERkUpr0SLo2xdefjl7e4cO\noUDb7rvHEpaIlDNpJ+NmVgPoBBxM2OZrBWHLrhfdfVPJhCciIiIiUjZs2gT3368CbSJSPNJKxs3s\ncGAC0CCXy4vMrJO7f1SskYmIiIiIlBH5FWi7/XaoVy++2ESkfKpSUAcz2wV4iZCIG6Ey+k+Jy8Du\nwCtmphqRIiIiIlKhrFwZlqQfdlj2RHz//eHtt8OydCXiIpKJApNxoD9Qj7AsvQdQy90bANsA/YC1\nwM7A30oqSBERERGR0uQOEyfCvvuGhDtRKb1mTRg0CN59V5XSRaRo0knGjyPMhvdz97HuvgHA3de5\n+3DgJsIM+fElF6aIiIiISOlYvBhOOQVOPx2+/z7ZfsIJ8MknMGCAKqWLSNGlk4w3ISTjk/K4PiGl\nn4iIiIhIubRpEwweHGbDUyulN2gATz8Nr7yiSukiUnzSKeC2LbDM3dfldtHdF1soG7lNcQYmIiIi\nIlJa3nkHLrpoywJtl1wCd9yh98JFpPilu7WZp9FHGzmIiIiISLmyYUNYdj50aPK9cAgF2h5+OBRu\nExEpCWnvMy4iIiIiUpH8+CN06QJvvZVsq1kz7CN++eV6L1xESla6yfj2Zja9CH3c3dsVIi4RERER\nkRLzwQfQsSN8802yrX17ePBBaKJKSCJSCtJNxrcC2hShTzrL3EVEREREStykSdCjB6xZE87NwnZl\nV14ZvhcRKQ3pJONjSjwKEREREZESlpUFt94KN9+cbKtTB556Ck48MbawRKSSKjAZd/fzSiMQERER\nEZGS8vvv0LMnTJyYbNtjD5g8GfbZJ7awRKQSUwE3EREREanQvvkGTj0V/vOfZNuxx4a9w7ffPr64\nRKRyqxJ3ACIiIiIiJeXf/4YWLbIn4pdeClOmKBEXkXgpGRcRERGRCmnUKDjmGPjpp3BevTqMHBn2\nFK+m9aEiEjP9NSQiIiIiFcqmTaEy+pAhybYddwxV1I88Mr64RERSKRkXERERkQpj+XLo1g1eey3Z\n1qxZKNTWuHF8cYmI5KRl6iIiIiJSISxcCK1aZU/ETzstvDeuRFxEyhol4yIiIiJS7k2ZAoccAl98\nkWy76SaYMAFq144vLhGRvGiZuoiIiIiUW+4weDAMGBC+B6hVC8aMga5d441NRCQ/SsZFREREpFxa\ntw4uvhieeCLZtuuu4f3w5s3ji0tEJB1FTsbNzIA/AbXc/ZuihyQiIiIikr8ffgjvg8+dm2xr3TpU\nTN9pp/jiEhFJV8bvjJvZYWY2GVgFLAO+ynG9npk9ZmaPmlmNIsYpIiIiIgLAggXQsmX2RPz88+GN\nN5SIi0j5kVEybmZ9gFnAycA2gEVff3D3FYQZ8/OADkULU0REREQExo8Pe4UvWRLOq1QJ+4k/+ijU\n0PSPiJQjhU7GzawVMATYDAwAdiXMjOfmcUKS3iXTAEVEREREsrLgH/+A7t3Du+IA9erB1KnQrx+Y\n5T9eRKSsyeSd8csJCfZN7n4PgOX9t9/M6Ngqg+eIiIiIiPDbb3DOOaEwW0LTpuF8r73ii0tEpCgy\nScaPjI4jCuro7ivMbBXQKIPniIiIiEgl99VX0LEjfPJJsq1DB3jqKahbN764RESKKpN3xncAVrn7\nqjT7e4bPKRIza2pm/c1srJktNLMsM3Mzy3fHSTPbzcweNLOvzGy9mf1kZq+Y2XFpPPMsM5ttZivN\nbLWZLTCzPmaW689vZjOimHrmcb2RmX0W9ZltZvonR0RERCqNGTOgVavsifiVV8KLLyoRF5HyL5Mk\neSWwbToV0s2sAVAX+CmD5xRVL+B+4GygKTkKzOXGzA4B/hONNeBl4EugPfCqmQ3IZ+wDwJNAC2A2\n8BqwFzAcmGhmVQsTvJntAbwF7A1MA9q7+8rC3ENERESkvBoxAo47Dn75JZxvtRWMGQN33w1VC/Vb\nlYhI2ZRJMv4BIVFtk0bfS6LjvAyeU1QfA3cD3YA9SL6/nisz2xqYCGwHDAX2cPfT3P1Q4Fjgd2CQ\nmR2Wy9guQG9gKdDM3U92987AnsBnQGegb7qBm9n+hIS+cRRTR3dfk+54ERERkfJq40bo1Qt694ZN\nm0JbgwYwcyb06BFvbCIixSmTZPwJQjJ+Z37Lps3sHOAfhGXqozILL3Pu/qi7D3D3Z9z9yzSGdCa8\n2/4VcKW7b06515vAvdHp9bmMvTY6Xu3un6eMW0aYZQe4Jq/l6qmi2fmZQAPgMaCbu29II34RERGR\ncu3nn+H44+Ghh5JtBx8M8+fDoYfGF5eISEnIJBkfC7wBNAfeNbMbgK0BzOxkMxtgZvOAMUBV4Hl3\nn1JcAZegltFxhrtvzOX669HxODOrk2g0s0bAwcAGYELOQe4+E1hCSK7z/WfEzNpGz9kOuNfdL3D3\nrML+ICIiIiLlzccfh/fDZ8xItp15JsyaBY1UClhEKqBCJ+Pu7oRZ5BeAJsDNQCI5fQG4k5DYGvAs\ncG5xBFoKakfHn/O4nmivDvwlpf3A6PiJu6/NY+z8HH23YGYdgVeiOG5y9ysKjFhERESkAnjhBTjs\nMPj662Tb7bfDuHFQq1Z8cYmIlKRMtjbD3VcDnc2sHdATOAxoSEjulwFzgNHuPq2Y4iwNP0bHJnlc\nT23fHXg75XuAxfnc+5scfXPqSigSVxXo5+7D8g81iKqw90ynL2Elg4iIiEiZ4Q533AHXp7wEWLs2\njB0Lp54aX1wiIqUho2Q8wd3fICxZrwimE95xP8nMGrn7dzmuX5LyfZ2U7xMz6r/nc+/V0XHbPK6f\nFB2fSTcRj/wZOLoQ/UVERETKhDVr4G9/g/Hjk2277w6TJ8Nf/pL3OBGRiqJIyXhF4u7TzWwWcBRh\nG7O+hOXlDYGrCAnzJsKfWep73Ikt07wIj0889wwze8fdB6c5bhEFVIlP0ZywzZyIiIhIrL77Djp1\ngnffTba1aQMTJsAOO8QWlohIqVIynt3pwCTgCLac8R9GSJgPAH5Naf8tOtYmb4lrv+Vx/XHCFmZD\ngXvMzN393jz6/sHdRwOjC+oHYGYz0Cy6iIiIxGzu3JCIL1uWbOvVC4YMgerV44tLRKS05ZuMm1mx\n7ebo7k8U171Kirv/aGZHEfYVPwbYgfAu+QvAe8CKqOtHKcMWRcfG+dx61xx9c3v2MDODkJAPNjPS\nSchFREREyosxY+Cii2BDtGlrtWowdGhIxkVEKpuCZsZHU7Tl16nKfDIOf1SLfy36+kOUpNcmFGP7\nb8ql96PjfmZWM4+K6i1z9M3r2TkTcnf3+wr/U4iIiIiUHZs3w9VXw+CUF/H+9KewLP2YY+KLS0Qk\nTgUl47MovmS8vLsmOj4QJewAuPu3ZvYecBBhmXu2Dx3M7GigEbCUUGU+XzkS8nujGXIl5CIiIlIu\nrVgB3bvD1KnJtr/8JWxn1iSvPWxERCqBfJNxd29TSnGUCWa2P/Clu69JaasJ3AV0AD4A7s9l6J3A\nBGCQmb3t7l9EY+sDD0Z9Brp7Vi5jt5BLQu7unttzRURERMqs//0POnaE/6asKezYMWxdtm1ee8yI\niFQSFbaAm5kdRDIRBtg3Ot5hZlcmGt390JQ+VwBdzexd4HvCsvTWwHaE98Q7uPuGnM9y94lmNgLo\nBXxkZq8DG4F2hG3QngeGFyb+HAn5fdEMuRJyERERKRdefRW6dQsz4wn/+Af8859QpUp8cYmIlBUV\nNhknJMGH5NK+Zz5jngd2JFRMPxRYA3wGjAceyi0RT3D33mb2FtCHULW8KrAQGAWMSHdWPMc9lZCL\niIhIueIeirJdfjlkRb/9bL01PP44nHlmvLGJiJQlFTYZd/cZJPcAT3fM84SEPNNnjgPGFaJ/mzT6\nDCNsqyYiIiJSpq1fD717w6hRybZddoHnn4cWLeKLS0SkLMo4GTezRsB5hGXcOwPbkHfy6+7+f5k+\nS0RERETKtmXLoEsX+Pe/k22HHALPPQcNG8YXl4hIWZVRMm5mZwMjga3JJwFPuaaK7CIiIiIV1Pvv\nw6mnwrffJtt69ICHHw5L1EVEZEuFLp8RFUZ7HKgZHTtHl34FjgXOjto3AD8D5wBtiyNYERERESlb\nJkyAI45IJuJVqsA998Do0UrERUTyk8nM+OXRuPvc/QqAqMjYBnefHvV5yszuB14FbiPswS0iIiIi\nFURWFtxyS6iOnlCnDowfDx06xBeXiEh5kcnGEkcQlp3fl6M923J1d/+IUFn8z8A1mQQnIiIiImXP\n6tVw+unZE/E994R585SIi4ikK5NkfCdgnbt/l9K2mbBsPafJhOXqnTJ4joiIiIiUMYsWQevW8Oyz\nybbjjguJ+N57xxaWiEi5k0kyvhrIuWf2SmBbM6uV2ujum4D1wK6ZhSciIiIiZcXs2dCyJXz4YbLt\nssvglVdgu+3ii0tEpDzKJBlfAtQys9S/cv8bHQ9P7Whm/wdsC2zMLDwRERERKQseeQTatYOffw7n\n1avDY4/BffdBtYw3yxURqbwyScbnR8dmKW1TCe+M32FmDQDMbAfgEcL75XOLEqSIiIiIxGPTJrj0\nUrjoItgYTa/suCNMnw7nnx9vbCIi5VkmyfjzhMT73JS24cCPwMHAN2a2BFgKtCEsab+9aGGKiIiI\nSGn79Vc44QQYPjzZ1rw5LFgQtjMTEZHMZZKMvwqcAvwr0eDuywl7iS8gbHvWMLr3d8Dp7j676KGK\niIiISGn59FNo1QreeCPZ1rUrvPUW7LZbfHGJiFQUhX7Dx903Ai/n0v4pcIiZ7Qo0IhR1+8zdvchR\nioiIiEipefll6N4dfvst2XbLLXD99VAlk6kcERHZQrGX23D3b4Fvi/u+IiIiIlKy3OHuu+Gaa8L3\nALVqwRNPQJcu8cYmIlLRqPaliIiIiLBuHVx4IYwdm2zbbTeYPBkOOCC+uEREKqpiS8bN7BTgfGAv\nYAPwH2C4u79bXM8QERERkeL3/ffQuTO8806y7YgjYNIkqF8/vrhERCqyAt/6MbN9zew9M3vLzGrk\n0eduQpX1jsA+wAFAD2CumZ1TnAGLiIiISPGZPx9atsyeiF9wQSjcpkRcRKTkpFOC41igOfC5u6/P\nedHMOgBXELY7W0uotv4csAqoCjxsZqq5KSIiIlLGPPkkHHlkmBkHqFoVhg6FkSNhq63ijU1EpKJL\nJxk/GnBgYh7Xr4qOvwAHuvsJ7t4F2Bv4AtgauKCogYqIiIhI8di8ORRpO+ccWB9NtWy3HUydCpde\nCmbxxiciUhmkk4zvFR3/nfOCmdUFjiIk67e5++eJa+6+DLiFMGPeruihioiIiEhRrVoFnTrBoEHJ\ntr33hnnz4Nhj44tLRKSySaeAWwPgN3dfkcu1QwkJvQOTcrk+OTrumVl4IiIiIlJcvvwSOnaETz9N\ntp14IowbB3XrxheXiEhllM7MeJ18+rWMjovd/bucF939N+A3QH+9i4iIiMRo+vRQqC01ER8wIGxd\npkRcRKT0pZOMrwS2MbN6uVw7JDrmt32ZAZsLG5iIiIiIFJ07PPAAHH88LF8e2mrUgH/9KyxVys9X\nXAAAIABJREFUr1o13vhERCqrdJLxj6Pj6amNZlYbaENYoj47t4Fmth1QG1iaeYgiIiIikokNG+CS\nS6Bv31C0DaBhQ5g5MxRvExGR+KSTjD9PmN2+2cwOBDCzrYChwDZAFvBsHmNbR8dP87guIiIiIiXg\np5/guOPCNmUJLVqEfcUPOSTvcSIiUjrSScYfAb4CGgILzOx7wh7ifyXMio/N7X3xyBlRn7eKIVYR\nERERScOHH4b3w2fNSraddVY432WX+OISEZGkApNxd18LnECY3TZCdfWtou9nAZfmNs7MdgK6RqdT\niiNYEREREcnfc8/B4YfD4sXh3AzuvBPGjoWaNeONTUREktLZ2gx3/8LMDgCOBfaPmhe4+4x8hu0A\nXAVsdPcPihSliIiIiOTLHW6/HW64IdlWu3bYtuyUU+KLS0REcpdWMg7g7puBadFXOv0/AT7JMC4R\nERERSdOaNdCzJ0yYkGxr0iRsW7bffrGFJSIi+Ug7GRcRERGRsufbb+HUU+H995NtxxwTEvM//Sm+\nuEREJH/pFHATERERkTLo7bdDhfTURLxPH5g2TYm4iEhZp2RcREREpBx6/PEwA/7jj+G8WjV46CEY\nPhyqV483NhERKZiWqYuIiIiUIx9/HKqjjxuXbPvTn2DSJDj66PjiEhGRwqmwM+Nm1tTM+pvZWDNb\naGZZZuZm1rWAcY3MbJiZ/dfM1prZOjP73MweMrMmBYw9y8xmm9lKM1ttZgvMrI+Z5frnbGYzoph6\n5hPLZ1Gf2WZWN+0/ABEREalQ3norVEXff//sifj++8P8+UrERUTKm4o8M94L6F+YAWZ2IDAdqAd8\nR7JyfAvgYuBsM2vv7m/nMvYBoDewDngD2Ai0A4YD7czs9Kgifbqx7AG8DjSO4jjN3dcU5ucRERGR\n8i0rC15+GQYODO+H59S1K4waBdtuW/qxiYhI0VTYmXHgY+BuoBuwBzAzjTEPEBLxR4Am7t7J3TsB\nuwOjgNrAiJyDzKwLIRFfCjRz95PdvTOwJ/AZ0Bnom27gZrY/MJuQiE8EOioRFxERqTw2boQxY8Ks\nd8eOWybinTrBnDmhYroScRGR8qnCzoy7+6Op52aWb38z2xo4LDq90d03ptxro5ndAJwPNDOzWjmS\n42uj49Xu/nnKuGVm1guYAVxjZsPcPauAOA4BpgDbAY8BFxU0RkRERCqG1avh0Ufh3nvDlmWpqleH\nc8+Fq66CvfeOJz4RESk+FTYZz8BmYBPhzyS3zN2j4+/A2kSjmTUCDgY2ABO2GOQ+08yWALsAhwK5\nLDL7415tgRcIM/D3uvsVGf0kIiIiUq78/DMMGxYqof/6a/ZrtWvDxRfDZZdBo0bxxCciIsWvSMm4\nmVUjLAHfDsh3Ew13n1WUZ5W0aPb7DaA9cIuZ9UnMjptZdeC2qOtj7u4pQw+Mjp+4+1pyN5+QjB9I\nHsm4mXUEngFqADe5+z+L9AOJiIhImbd4MQweHGbD1+b4LWLHHaF/f+jdG7bbLp74RESk5GSUjJvZ\n7sCdQEdC8lgQz/RZpaw3MBW4EOhgZgui9paEDxyGAFflGLN7dFycz32/ydE3p66EDwGqAv3cfVg6\nwUZV2Hum0xdonmY/ERERKWEffQSDBsH48bA5R3nX3XeHK6+E886DmjXjiU9EREpeoRPkqMr3HGB7\nwnJuB34kVBEv19z9KzM7HHgC6ACkLgZbAMxKfZc8Ujs6/p7PrVdHx7xKrJwUHZ9JNxGP/BnQRiYi\nIiLlgHvYnmzgQHjllS2vH3AAXHNNqJBerTxMYYiISJFk8lf9rcCfCFt/XQZMdvdNxRpVTKJE/Flg\nFXAq8G/CBw6tgcHAJDPLuYQ88X556tL1wpoFHAWcYWbvuPvgNMctIr0q8RBmxrVPuYiISCnLyoIX\nXwwz4XPmbHn9mGPg6qvh+OOhgHqzIiJSgWSSjLclJJ7d3f3fxRxPbMysHvA8sA1wuLt/lXL5BTP7\nBPgQuMHMnkqpmv5bdKxN3hLXfsvj+uOELcyGAveYmbv7vQXF7O6jgdEF9QMwsxloFl1ERKTUbNgA\n48bBXXfBZ59lv2YGnTuHJLxVq3jiExGReGWSjG8LrK1IiXjkJGBHYHqORBwAd//CzOYBbaKvRDK+\nKDo2zufeu+bouwV3HxZtvzYUGGxmpJOQi4iISNmyejU88kjYnuy777Jfq14devQI25M1bRpPfCIi\nUjZkkox/A+xm0fRtcQcUo92i48p8+qyIjtuntL0fHfczs5p5VFRvmaNvrnJJyN3d78s/bBERESkL\nfvopuT3Z8uXZr227LVxySdiebOed44lPRETKlioZjBlPqKDerphjidv30fHgaCuzbKK2g6PTrxPt\n7v4t8B6wFXB6LuOOJhSCW0oofJevqIBbv+j0XjP7eyF+BhERESllX38NfftC48Zw663ZE/H69eGO\nO+Cbb8JydSXiIiKSkEkyPhD4AHg42uKsopgCrCHMkN9nZn9s2RZ9P5Sw3Hw5MC3H2Duj46Co2nxi\nXH3gweh0oLtnpRNILgn5ZYX8WURERKSEffghnH027LknPPBA9n3CmzSBESNg0SK49lqoVy+2MEVE\npIzKZJn6GYSCY7cAH5nZRGA+eRcnA8Ddn8jgWRkzs4NIJsIA+0bHO8zsypS4Do2OP5pZb+AxoA/Q\n2czeJVRLPxhoCKwHznf3bEvZ3X2imY0AehH+TF4HNhJWD9QhFIYbXpj4cyxZvy96h/z+wtxDRERE\nipc7zJoVKqNPmbLl9QMPDEXZunTR9mQiIpK/TP6ZGE2opp7YfOPc6KsgpZqME5LgQ3Jp3zOvAe4+\nxsw+ImzZdiRwfHRpCSFJv9fdP81jbG8ze4uQyB8NVAUWAqOAEenOiue4pxJyERGRMiArCyZPDkn4\n3LlbXm/bNuwRfuyx2p5MRETSk0kyPoui7aldKtx9BskPDAoz7j2gR4bPHAeMK0T/Nmn0GQYMyyQe\nERERKZoNG+DJJ8P73gsXZr9mFmbABwyAli1zHy8iIpKXQifj6SSQIiIiIuXZb7/ByJFw332wZEn2\na1ttBX/9K1x5Jey1VzzxiYhI+ae3mUREREQiP/4IQ4eGgmwrVmS/VqdOcnuyhg3jiU9ERCoOJeMi\nIiJS6X31FdxzDzz+OKxbl/3aTjvB3/8eEvG6deOJT0REKh4l4yIiIlJp/ec/oSjbM8+EIm2p9tgD\nrroKevSArbeOJz4REam4Mk7GzawlcAnQGtgZ2Caf7u7uSvxFREQkdu4wY0ZIwqdN2/L6wQeH7clO\nOw2qVi318EREpJLIKEE2s2uA24Aq6Q7J5DlS/FatijsCERGReGRlwfPPhyT8nXe2vH7ssWF7srZt\ntT2ZiIiUvHST6T+Y2THAHYTtzW4EDoou/QTsQZgpvwn4Ofo6Fdi9OIKVovvoI/jvf+OOQkREpPSs\nXw+PPQb77BO2IktNxKtUgdNPhwUL4LXXoF07JeIiIlI6MpkZv5SQiN/k7ncAWPhXa7O7fwV8Bcwx\ns0eBGcBjwIHFEq0U2aZN0L49zJmjSrAiIlKxrVqV3J7s+++zX6tRA3r2DNuT7bFHLOGJiEglV+iZ\nceCQ6Dgyv3u5+w9Ab2AH4LoMniMlZPFi6NBBS9ZFRKRiWrYMrrsOdtstFGBLTcTr1AlL0Rctgoce\nUiIuIiLxyWRmfAfgd3f/OaVtE1Arl77TgbVAhwyeIyXogw9CYZpXXoGttoo7GhERkaL78svk9mTr\n12e/1qBB2J7s4ou1PZmIiJQNmcyML2fLJH45sI2ZZfvnzd0dyAK0ILqMaNo0+f0bb4Qlejm3chER\nESlP3n8fzjwT9torzHanJuJ77gmPPBJmwgcMUCIuIiJlRybJ+HdADTPbMaXt0+jYJrWjmR1A2PLs\n94yik2LXoAHcdlvy/Kmnwi8nIiIi5Yk7TJ8e6qAcdBA8/XT2D5dbtoSJE+Gzz+CCC8I74iIiImVJ\nJsn4v6Nji5S2yYTty+4xs5ZmVt3MDgLGEIq9zSxamFKcrrsOLrkkeT54cChuIyIiUtZt3gyTJkGr\nVqHy+auvZr9+/PFh5de8eaFyuvYJFxGRsiqTZPw5QuL915S2EcDnwP8Bc4F1wHygGeGd8ZuLFKUU\nKzMYPhw6dUq2XX55mFUQEREpi9avD8vN99kHunYNW5ElVKkC3brBe+/BtGnaJ1xERMqHTJLxWcD+\nwA2JBndfBxwNTAA2EJJ1gDlAW3f/qIhxSjGrWhXGjYPWrZNtPXrAm2/GF5OIiEhOq1bBXXfB7rvD\nRRfB558nr9WoAb16wf/+B+PHw4HaSFVERMqRQldTd/cs4JNc2pcC3cysOqHi+ip317viZVjNmjB5\nMhxxRHinbsOGMFs+axYccEDc0YmISGW2dCkMGQIjRsDKldmv1a0LffpAv36w007xxCciIlJUmWxt\nli933wj8UNz3lZKx/fYwdSocdljYh3XVqrAH+Zw50Lhx3NGJiEhl88UXYXuy0aO33J5s553D9mQX\nXRT2CxcRESnPMlmmLhXMbrvBlCnJX2x++AFOOAF++SXeuEREpPJ4910444ywBefDD2dPxJs2hUcf\nha++giuvVCIuIiIVQ74z42a2W/TtRnf/IUdbobj7N5mMk9LRrBk8/3xIwjdsgIULoWNHeP31sJxd\nRESkuCW2Jxs4MPx7k1OrVnDNNXDqqaFIm4iISEVS0DL1r6PjQmC/HG2F4Wk8S2J2zDHwxBNw5pnh\n/O23oXv3sIWMtoYREZHisnkzPPssDBoUZsRzat8+JOFHH62q6CIiUnEV9DmzRV9VcmkrzJc+zy4n\nunXLvuf4Cy9A375h9kJERKQo1q2DkSNh773DkvTURLxKlfAB8Pvvh1ombdooERcRkYot39lqd98i\nic6tTSqWyy6DJUtCAR2Ahx6CXXaB66+PNy4RESm/pk+Hc84JdUlSbb01nH8+XHEFNGkST2wiIiJx\n0NJxydWgQeEXpiefDOc33BCq2J5/frxxiYhI+fPSS9C1a/aibPXqhZVXl14K9evHF5uIiEhclIxL\nrqpUgVGjYNmyZFGdiy4K+7medFK8sYmISPkxcWJYfr5pUzivXx+uvhouvBC23Tbe2EREROKkJeeS\np622CsXbmjcP55s3h3f83nkn3rhERKR8GDs21CJJJOJNmoR/Qy6/XIm4iIhIQVubjSqm57i7/62Y\n7iWlqE4deOUVOPxwWLQI1qwJM+P//jfstVfc0YmISFn1yCNw8cXJAqB77x1WWu2yS7xxiYiIlBUF\nLVPvSdiWLLd6pqn1tXNez3nNASXj5VTDhqGybevW8Msv8PPPYT/yOXPCsnUREZFUQ4dC//7J82bN\n4LXX9G64iIhIqoKS8VvyaN8K6A3UBRYDs4AlhMS7IXAU8GdgBfAQsD7Xu0i50bRpKMDTti2sXQtf\nfw0nnggzZmipoYiIJA0aFPYIT2jRAqZNg+23jy8mERGRsqigrc22SMbNbCvgzWjsue7+ZG5jzaw7\nMBI4EmhX9FAlboceCk8/DZ06QVYWvPdeqI774ovh/XIREam83OHmm+Gf/0y2tW4NL78MdevGFpaI\niEiZlUkBt2uAQ4FeeSXiAO7+FNALaA0MyCw8KWtOOQUefjh5/uqrcMEFyXcCRUSk8nGHAQOyJ+Jt\n24ZXnJSIi4iI5C6TZLw7sAF4Ko2+4wlL1M/K4DlSRl1wQZj9SPjXv+Daa2MLR0REYpSVFfYLv+ee\nZFuHDuHVptq144tLRESkrMskGW8MrHP3zQV1dPdNwLpojFQgN94Y9ohNGDQIhg2LLx4RESl9mzeH\nfwsefDDZ1rkzPPcc1KwZX1wiIiLlQSbJ+G9AHTP7S0EdzWx/QpG33zJ4TpGYWVMz629mY81soZll\nmZmbWdc8+reJrqfztVse9zjLzGab2UozW21mC8ysj5nl+udsZjOi+/XM43ojM/ss6jPbzMrMYj+z\n8MvXKack2/r3h4kT44tJRERKz8aNcO65MCplE9Tu3UNtkRo14otLRESkvCiomnpupgPdgFFm1t7d\nl+fWyczqAY8RtjWbnnmIGesF9C+wV9JSYEw+11sB+wBfAt/mvGhmDxAqzK8D3gA2EgrXDQfamdnp\n6awmSLnfHsDrhFUF04DT3H1NuuNLQ7VqMH48tGsHc+eGdwbPOSdsXXPUUXFHJyIiJWX9+pB4P/dc\nsu3882HkSKhaNb64REREypNMkvGbgFOAg4H/mtlIwtZm30fXdyZsbXYhsCOwJhpT2j4G7gYWAO8S\nPhg4Oq/O7r6QsK96rszsk+jbUe7Zy5WZWRdCIr4UOMrdP4/adyJUnu8M9AWGpBN4tKLgVaABMBE4\n2903pDO2tNWqFaqpt24N//tf+AWtY0d46y34S4FrJ0REpLxZuxa6dIEpU5JtffqEvcWrZLLeTkRE\npJIq9D+b7v4/4ETgZ2AH4FpgCvBB9DUlatsR+BE4MZGcliZ3f9TdB7j7M+7+ZVHuZWaHAfsCm8l9\n9jxRvuzq1J/V3ZcRZugBrslruXqOZx0CzCQk4o8B3cpqIp6www5hD9kGDcL5ypVwwgnw7RbrB0RE\npDxbvRpOOil7In7llaFmiBJxERGRwsnon053nwU0Jcx4fwRkARZ9ZUVtNwB7R33Lu/Oj41R3X5J6\nwcwaEVYJbAAm5Bzo7jOBJYTk+tD8HmJmbQlL07cD7nX3C9w9q+jhl7w//zn8crbttuF8yZKQkC/P\n9SUGEREpb1auhPbt4c03k2033gh33RXqiIiIiEjhZPw5truvcPdb3b05UAtoGH3Vcvfm7n67u68o\nrkDjYma1CO/IQ5ipzunA6PiJu6/N4zbzc/TN7TkdgVeA2sBN7n5FBuHGqnnz8P5g9erh/NNP4dRT\nYd26eOMSEZGi+eWXUB/k7beTbQMHwi23KBEXERHJVLEsKnP3je6+LPraWBz3LENOB7YlLLl/KZfr\nu0fHxfnc45scfXPqCkwCtgL6ufs/M4izTGjXDkaPTp7Png1nnx22vxERkfLnxx/hmGPg3XeTbUOH\nwtVXxxeTiIhIRZBJAbdsoiJluxJmxCvCkvScEkvUn8jjg4ba0fH3fO6xOjpum8f1k6LjM+6e9m7d\n0ZZoPdPs3jzd+xbVWWfB99/DVVeF82efDdueDRumGRQRkfJkyRI49lhYuDCcm8HDD4e9xUVERKRo\nMk7Gzawb8A9gv6jJU+8XbW02gfAeeWd3L/W9xosq2l4ssUnXqLy6RUfP43o6ZkXPOcPM3nH3wWmO\n+zP5VIiP0xVXhF/i7r8/nD/wADRqBNdcE29cIiKSnkWLwmqnr74K51WqwJgxYQtLERERKbqMknEz\nGwhcRUhE1wPVSSalQHin3MyWAmcBHYEnixZqLBKz4nPc/bM8+iQ+ZKidx/XUa3l9IPE4YQuzocA9\nZubufm8a8S0iVF5PR3Ogbpp9i8wMBg+GH36Ap58ObddeCzvvDD16lFYUIiKSic8/D4l4YleMatXg\nqaega9d44xIREalICp2Mm9nxwABgJWEv8eeA74D6uXQfA5xN2Ge7XCXjZlYVSKSNuRVuS1gUHRvn\n02fXHH234O7DLKzhHgoMNjMKSsjdfTQwOr8+CWY2g1KeRU/Movz4Y7L67t/+BvXrh0rrIiJS9nz6\naUjEly4N5zVqwKRJYUszERERKT6ZFHDrS1iSfZW7T3T3/EpzzYn6HpRJcDFrD+xCeBf86Xz6vR8d\n9zOzmnn0aZmjb66i98X7RaeDzezvacZaZtWoESqsN2sWzjdtCjMrCxbEG5eIiGzpP/+Bo49OJuI1\na8JLLykRFxERKQmZJOOHRMdxBXV0998JM+gNMnhO3P4WHZ9299V5dXL3b4H3CJXQT8953cyOBhoB\nSwkfTuQrR0J+b0VIyOvWDXuQ77ZbOP/99/CL3ZdfxhuXiIgkzZsXqqb//HM4r10bpk0LBdxERESk\n+GWSjNcDVrn7mjT7V83gGbEysx2Ak6PT/JaoJ9wZHQdFRd8S96kPPBidDnT3rHSen0tCflk648qy\nnXeGqVNhu+3C+Y8/Qvv24SgiIvGaNSsk3StWhPN69eCNN+DII+ONS0REpCLLJBn/FahjZrUK6mhm\nuxO281qawXOKxMwOMrO5iS+SS+XvyNGem3MJM90L3f3tgp7l7hOBEYQVAB+Z2Ytm9izwObAv8Dww\nvDDx50jI76sICfk++8CLL8LWW4fzL78MM+Sr81x3ICIiJe3110Mdj8TfxTvsEOp8tGoVb1wiIiIV\nXSbJ+DvR8eR8ewVXRMfZGTynqOoQltQnvhJ7fO+Zoz0350XHvLYz24K79yYUq3uPUCitPfAF4R37\nLgW8W5/XPStcQt66dajIWyX6L2/BAjjjDNiY2w7uIiJSol56CU4+GdauDecNGsDMmdC8ebxxiYiI\nVAaZJOOPErYxu8PMcq0gbmZVzex6oDehgNtDmYeYGXef4e5W0FceY5tF1+8u5DPHuXtrd6/j7tu4\n+8Hu/kBey9PdvU30nNH53HNYSrz3FyaesqpTp7DveMKUKXDRReBF2aldREQKZeJE6NwZ1q8P57vu\nGpar77tvvHGJiIhUFoVOxt39RULxtibAe2b2GLANgJn1NbMHCVt43RINGeHuBRYuk8rlkkvg+uuT\n56NHww03xBaOiEilMnYsdOsWdrgAaNIEZs+GPfeMNy4REZHKJJOZcYCewBCgLmFJd+2ofQhwMWFL\nMAcGk1xmLZLNP/8J552XPL/9dhgxIr54REQqg0cegR49ICtas7X33mFGvHGua91ERESkpFTLZJC7\nbwL+bmYPAH8FDgMaEpL7ZYQtvMa4+8LiClQqHjN4+OGwn+2UKaGtT5/wzmLnzvHGJiJSEQ0dCv37\nJ8+bNYPXXoP69eOLSUREpLLKKBlPcPcvAC0uloxVrw4TJoS9befPD++Nd+8eqvsecUTc0YmIVBwD\nB8K11ybPW7QI+4hvv318MYmIiFRmmS5TFyk222wDL78Me0Q7tK9fDx07wqefxhuXiEhF4A433pg9\nEW/dOnzoqURcREQkPkrGpUzYcccwQ5NYKrl8edj3dsmSeOMSESnP3GHAALj11mRb27YwdSrUrRtf\nXCIiIpLGMnUz61EcD3L3J4rjPlJxNWkCr7wCbdrA6tXw7bfQoUMoLFSvXtzRiYiUL1lZcOml8OCD\nybYOHWDSJKhZM764REREJEjnnfHRhMroReGAknEp0MEHh18UTzopbLnz0UehmNvUqVCjRtzRiYiU\nD5s3w4UXwuOPJ9s6d4anntLfpSIiImVFYQq4LQfWllQgIgnHHw+jRoWtdwBmzAjfP/UUVNGLFSIi\n+dq4MfydOX58sq17dxgzJhTNFBERkbKhMMl4dWAy8IS7zyiZcESCc88N74snCg498ww0bAj33Re2\nRBMRkS2tXx8S7+eeS7adfz6MHAlVq8YXl4iIiGwpnXnG04DngRpAT+ANM/vazG4zs71KMjip3K6+\nGvr2TZ4PGQKDB8cXj4hIWbZ2LXTqlD0R79MHHnlEibiIiEhZVGAy7u7Pu3sXoCHQF5gPNAauAz4z\ns3lm1tvMtEGKFCszuP9+6No12XbVVfDkk/HFJCJSFq1eHWptTJ2abLvyShg2TK/3iIiIlFVp/xPt\n7svd/UF3PxRoCtwBfAO0BIYB35vZs2Z2mpnprTQpFlWrwr/+BUcdlWw777ywP66IiMDKldC+Pbz5\nZrLtppvgrrv0Wo+IiEhZltHn5e7+ubtf7+67A8cAY4B1QCdgAvCDmZ1TfGFKZbb11vD887DffuF8\n48ZQFfj99+ONS0Qkbr/8Au3awdtvJ9sGDoSbb1YiLiIiUtYVefGau8909/OBBsAAYBOwHdCsqPcW\nSdhuu7D8slGjcL56ddgv9+uv441LRCQuy5bBMcfAu+8m24YODfU2REREpOwrcjJuZjWjWfAXgDsJ\nVdcN+K2o9xZJ1ahRSMjr1Qvny5aFpZk//xxvXCIipW3JEjj6aPjoo3BuFiqmX3ppvHGJiIhI+jJO\nxs2srZmNBpYRlqkfB/wCDAVauPutxRKhSIr99oMXXoAaNcL555/DySfDmjXxxiUiUloWLQp1NP77\n33BetSo88QRceGGsYYmIiEghFSoZN7N9zOxOM/sGeA3oQdirfCJwCrCLu1/m7u8Vf6giwVFHhYrq\nifch582Dbt1g06Z44xIRKWmffx7+Dvzqq3BerRo8/TScoyotIiIi5U6BybiZ7WBm/cxsAfAxcDXQ\nCHgbuBho6O7d3P1ld99csuGKBF26hHcjE156CXr1Avf4YhIRKUmffBIS8W+/Dec1aoTill26xBuX\niIiIZKZaGn2+B6oS3gP/EngCGOvuKp0lserbN7w3OXBgOH/0Udhll1BFWESkInn/fTj++GSNjJo1\nYfJkOPbYeOMSERGRzKWTjFcDHFgMzAf2AW63wu2Z4u5+duHDE8nfHXfA99+H9yUBbrkFdt4ZLroo\n3rhERIrLvHlwwgmwYkU433ZbePllOPLIeOMSERGRokknGYcwK94Y2C3lvDAcUDIuxc4szIgvWwbT\npoW2Xr2gQQPo2DHe2EREimrWLDjppLCdI4TdJKZNg1at4o1LREREii6dZHxMiUchUgTVq8OECcn9\ndrOy4Mwz4Y034LDD4o5ORCQzr70Gp54Ka9eG8x12CG3Nm8cbl4iIiBSPApNxdz+vNAIRKYrEss3D\nDw9VhteuDVuevf02NG0ad3QiIoXz4ovQtSts2BDOGzQIHzDuu2+8cYmIiEjxyXifcZGyZqedYOrU\nMHsE8Ouv0L49/PBDvHGJiBTGhAlw2mnJRHzXXcNydSXiIiIiFYuScalQ9twzzJDXqhXOFy+GDh1g\n1ap44xIRScfYseE1m02bwnmTJjB7dvi7TURERCoWJeNS4bRqBRMnQtWq4fyDD7LPMomIlEUjR0KP\nHqHuBcDee4cZ8caN441LRERESoaScamQOnQIVdYT3ngDevZM/pIrIlKWDBkCF18M7uEThrOVAAAg\nAElEQVS8WTOYORN22SXeuERERKTkKBmXCqtnT7jttuT5U0/BgAGxhSMikquBA+Gyy5LnLVrAm29C\n/frxxSQiIiIlT8m4VGjXXQeXXJI8HzwY7rsvvnhERBLc4cYb4dprk22tW8Prr8P228cXl4iIiJQO\nJeNSoZnB8OHQqVOy7fLLYfz4+GISEXGHq66CW29NtrVtG3aEqFs3vrhERESk9CgZlwqvalUYNy7M\nOCX89a9hGaiISGnLyoK+fcNKnYQOHeCll6B27fjiEhERkdKlZFwqhZo1YfJk2GefcL5hQ5gt/+CD\neOMSkcpl82a44AJ48MFkW+fO8Nxz4e8pERERqTwqbDJuZk3NrL+ZjTWzhWaWZWZuZl3TGFvTzAaY\n2XwzW2Fma8zsazObYGat8xhTxcz6mNkCM1ttZivNbLaZdc/nOYuimNrkcX1/M/sh6jPRzLZK+w9A\ntrD99mEJ6M47h/NVq8Js1OLF8cYlIpXDxo1wzjnw+OPJtrPOgmeegRo14otLRERE4lFhk3GgF3A/\ncDbQFLB0BpnZ7sCHwCBgN2Am8BLwE3AqcEwuY6oCzwHDgT2BV4G3gJbAODMbWtjgzewQYAbQAHgc\n6Obu2im7iHbbLSTkdeqE8x9+gBNOgF9+iTcuEanY1q+HM87IXq/i/PPhiSegWrX44hIREZH4VORk\n/GPgbuD/27vvMLuqsmHj95OEnoAEpAgKobdIRKq8GiBGlCogKqCg8tIFUaqigAUIAoJ0ERALoiAK\nUpRqAJFeXhGkKAQkBJAAgYQSIM/3x9rzzclwZnJmMjlzZnL/rmtfZ/bea629ZljM5NmrfQ5YiRJU\ndykiFgKuq9J/H1g2M7fNzM9m5vrA0sDFdbIeCGwDPASskpnbZ+aWwEjgOWD/iNi20YpHxGbA9cBw\n4MfA7pn5TqP51bWRI+Gyy2DeapzBww/DNtvA66/3bb0kDUyvv16mxVx2Wfu1/faDn/60rGkhSZLm\nTgM2GM/MczPz0My8ODP/3WC2bwMrAr/IzCMz860OZU7OzEdrr1W94m27V++Tmc/VpH8MOKw6PaKR\nCkTENsBVwFDgu5l5YGZmg/VXgzbdtPRItfnb32Cnncp8TknqLVOnwpZblhE5bQ45BE47DQYN2L/A\nkiSpEf5ToFLNx96jOh3XjawbAUsAT2fmzXXuXwK8BawXEcvMog67AJcC8wEHZubR3aiHuulzn5t5\nz/HLLy8rHPvqY87JhP/+F/76VzjvPDj0UNh2W1hnnbLC/d//3tc1lHrPlCmw+eYz79xw1FFw/PFl\n20VJkjR3c6Zauw8DiwH/ycx/RsRHgK2qa88Cf87M2+rk+1D1eVe9QjPztYh4EBhVHRPrpYuIfShz\nzpMyLP1n9dKpdx14IEycCCeeWM7PPhuWWQa+/e2+rVd/9+ab8O9/wyOPzHw8/DC89FL9PPfdV0Yr\nbLUVfOtbsNFGza2z1JsmTy6B+D33tF8bNw4OO6zzPJIkae5iMN5uZPX5WERcAOzW4f6REXEp8MXM\nrJ1dPKL67GpN7qcogfiITu4fSFkcbjqwU2b+vpEKR8SXgC81krZ6vuo4/viykNuFF5bz73ynrLj+\nla/0bb1aXSY891wJsDsG3U88UfZS7okrryzHJpuUoPzjH7cXUf3Lc8/B2LHwwAPt1049Ffbfv+/q\nJEmSWo/BeLvh1efHgMHAicDZwOTq2pnADsArQG2YNrT6nNZF2VOrz2Gd3G9b3O2URgPxyvLA6G6k\nVx2DBsH555d/QF9/fbm2556w5JJlrufc7vXX4bHH3h1wP/JI2R6uuxZcEFZZBVZdtf0YPrwsZvX7\nmtY/fnw51l23BOXbbuscW7W+iRNhzJjy/weUF0nnnFP2FpckSaplMN6u7Z/5Q4BzM/OQmnt/jIhn\ngDuB3SLiB5n5eHWvrc9udmYa30wJ+A+KiHsys96K7fVMoIFV4iujgEV6ULe5wrzzwqWXwujRcP/9\nZSG3z362zPVcf/2+rt2cl1mCiHq93E891f159BFlG7nagLvtWGaZ+kH1Jz8JDz1URipceGH7Ynp3\n3w3bbw9rrAGHH14W2nMrKLWiCRNKIP549ddh8GD4+c9hl136tFqSJKlF+U/adq/WfP3Tjjcz8+6I\nuAdYF9gEeLxDvqEd89Rou/dqJ/ePosxPP4iyLzmNBOSZeQFwwazSAUTEeOxF79LCC8PVV8NHPlL+\nUf3aa6Vn/NZbS0/uQDB1Kjz66LsD7kcfhWldje3oxLBh9QPulVcuPeDdtcYaJXj57nfhhBPKIm9v\nvlnuPfQQ7LorHHlkmXf7pS/B/PN3/xnSnPDYY7DZZvD00+V8yJCyp/gOO/RtvSRJUusyGG83oebr\nJzpJ8wQlGF+qTr7luij7/XWeMZPMPDjKxNiDgAsbDcjVu5ZeumxBtPHGZQGmF14oPba33VaGrfcH\nM2aU3uy2BdNqg+6JdZcP7NqgQTBiRP2ge6ml5sx87uWXhzPOKPP3Tz4ZzjyzvEiA8qJkn31KwH7Q\nQbDXXuWlgNRXHnywrG3w7LPlfL75ykgbp7lIkqSuGIy3u7fm68WA/9ZJs3j1ObXmWlu+9eoVGhEL\nAmtVp/d1VYE6AXlm5iWzqLd62aqrlgXENtuszJd+4gnYYosyf7mVgr4pU+rP437sMXjjje6Xt+ii\nMwfaq61WPldcsQQXfWGppcqw9cMPh9NPh1NOgRdfLPeefbbs13zssXDAAWVxrMUW65t6au51333w\niU+UF3dQRoRcfnkJziVJkrpiMF7JzIkRcQewATAGeLj2fkQsCqxTnd5dc+s24Hlg2Yj4WJ29xncE\n5gHuysxZ9kt2CMjbhqwbkDfZhhvCb38Ln/506Wm+9174zGfgiivK/PJmefvt0hPccXuwRx4pC851\n15AhJbiu18u9+OKtu2r5oouWXvKvf70s9HbiifDMM+XeSy+VXvITT4S994ZvfKOshi/NaXfcUUbO\nvPxyOR82DK66Cj760b6tlyRJ6h8Mxmd2DPBHyjZmt2bm/QARMT9wFmUBtHsoATgAmflORJwAnACc\nFRGbZubzVb6VgXE1ZTekTkCemfm72f7u1C1bbw0/+QnssUc5v/basiLyz3/e+0Hr5Mn1e7n/9S94\n663ul/fe97b3bNceI0bAPPP0bt2baejQEpDvu2/Zk3zcuPbFsqZNg5NOgtNOK/PJDz20vHiQ5oSb\nby7D0NumT7znPXDNNXPHgo+SJKl3DNhgPCLWoWxH1maN6vPYiDi47WJmbljz9RURcSJwMHBH1VM+\nGVgfeB8wkbIPeMe1pU+mrIa+NWWf8hsoveEfB+YHTsvMy7tT/w4B+UVVD7kBeZP97/+WedZHH13O\nf/nL0us6blyX2eqaPr0EjvV6uSdP7n55885bFkqr18u96KLdL68/mW++8pLky1+GSy4pQ9X/8Y9y\nb/r0spXUuefC5z8P3/wmrLVW1+VJ3XHddWWrvddfL+eLL16ujRrVt/WSJEn9y4ANxoGFKUPOO1q5\nq0yZeUhE/A3YH/gQsCDwFPAjYFxmvmsuedU7/mlgX+DLwObAO5Re9DMz89c9+QYMyFvDkUeWgPyn\n1Rr7xx9ftufaf/93p82E55+v38v9+OPt23V1x/veVz/gXm65snXS3GzIkLLV2ec+V4YHH3NMGToM\nZXrBr39djm23LUH5BvV+I0jdcMUVZcrK9OnlfOml4frry04AkiRJ3RHv7uTVQNS2tdno0aMZP358\nH9em/3n77bLX9RVXlPMIOOus0iPWMehumz/aHQssULZP6xhwr7JK2XJNjcksC+0de2wJkDoaMwa+\n9S3YdNPWnR+v1nXJJbDzzuX3AcAHPgA33AArrdS39ZIkSY3ZZJNNuOmmmwBuysxN+rg6A7pnXOo1\nbXsGjxkDt99egr699+5+OR/4QP1e7mWXLVuIafZElEB7003hzjvhuOPgssva799wQzk22KAE5Vtt\n5c9djfnlL8taBDNmlPMVVoAbbywjVCRJknrCYFxq0IILlp7xjTeGRx/tPN3QofW3CFt55VKGmmP9\n9eEPfyh7QI8bBxdd1D5N4I47ytD1tdYqw9c/+9nywkWq55xzysu3toFkq61WRl4ss0zf1kuSJPVv\nDlOfSzhMvfdMmAC77Va21qq3gNrSSzsEuhU9/jiccAKcf377fN82K6wAhx1W/rv21Z7qaj1TpsCZ\nZ5ZRFG0++MGyWNsSS/RdvSRJUs+02jB1g/G5hMG4VDzzDJx8cpnzP23azPeWXhoOPhj23LOMcNDc\nJRP++c+yGODVV8Nf/9o+Pxxg3XXL9mXDh/ddHSVJUs+1WjDubElJc5X3va/0kD/1VNmyrnYbuEmT\n4KCDyjzg730PXnyxz6qpJnn9dfjTn+CrXy0jJNZcs+xRP378zIH4xhuXoekG4pIkqbcYjEuaKw0f\nDkcdBU8+CSeeWHrF27z4Yrm33HIlMJs0qe/qqd731FNw9tmw9daw2GKwxRZwxhllCkpH665bVue/\n9lpYZJGmV1WSJA1gBuOS5mrDhpXe8McfLwHaiBHt96ZOLb3oI0bAvvvCE0/0XT3Vc2+/DbfcAocf\nDiNHlpcs++wDV15ZesZrDRsGO+xQ1haYNAnuuqss8ufii5Ikqbe5frAkAfPPD3vtBbvvDr/9bdkW\n7cEHy7033yxzzM85p+wzffjhsMYafVtfde2FF+DPfy7zv6+5Bl56qfO0q60GW25Zesj/539g3nmb\nV09JkjT3MhiXpBpDhsAuu8BOO5Wt7I49tuxZDmVrtF/+shzbbVd6TNdbr2/rqyIT7r+/ffG1229v\n34qso/nmg002KQH4lluWueKSJEnNZjAuSXUMGlT2It9mG7jxxhKU33hj+/0//KEcY8eWra9Gj3ZL\nu2abOrUsqtYWgD/zTOdpl1229HxvuSWMGQMLLdS8ekqSJNVjMC5JXYgowduYMaW39bjj4I9/bL9/\n3XXl2GijEpRvuaVB+Zz02GMl8L7qKrjppnfvGd9m0KDy36St93vkSP+7SJKk1mIwLkkN2nBDuPxy\neOABGDcOfvMbmDGj3LvttrI69wc/WIav77gjDB7ct/UdCKZPh5tvLsH3VVeVYLwzw4fDJz9Zgu/N\nNy8rpUuSJLUqV1OXpG4aORIuvBAefRT23HPmBb/+/vcy33y11eDcc8vib+qeSZPgvPNg++1LQD12\nLJxySv1AvO3lx1//Cs8/X/677LyzgbgkSWp99oxLUg+tuCL85Cdw5JHwox+VrdFee63c+9e/YI89\n4Oij4eCDy9fOU67vnXfKFmJtc7/vvbfztAsuCB//ePvq58su27x6SpIk9SaDcUmaTcssAyedVOaM\nn3pqOV5+udybOBG+/nX4wQ/gwANhv/1g0UX7tr6t4OWXy5ZjV10Ff/pT2YqsMyus0D73e/Tosg2d\nJElSf2cwLkm9ZLHF4LvfLT3hZ59dAvTnniv3Jk+G73wHfvhD2HffEqAvuWTf1reZMsu+7W2Lr916\na+kRr2fIEPjYx9p7v1dd1cXXJEnSwGMwLkm9bNgwOOQQ2H9/+NnPSgA+YUK59+qrcPzx8OMfw+67\nl3TLLden1Z1jXnsN/vKX9uHnTz7Zedoll2zfemzsWFh44ebVU5IkqS8YjEvSHDL//LDPPvC//1tW\nXj/uOPjnP8u9N96AM84oc8532QUOOwxWX71v69sbnnyyfeXzG28s32dn1luvffj5OuuU7cgkSZLm\nFgbjkjSHzTMPfPGLJei+/HI49li4++5y7+234ec/h1/8oqwe/s1vwoc/3Lf17Y633oK//a299/vB\nBztPu/DCZcuxLbcsW5DNTcP0JUmSOjIYl6QmGTQIttsOPv1puP76EpSPH1/uZcKll5Zj883LYnAf\n/WhrzpX+73/LomtXXVUWYZsypfO0q6/e3vu98cblxYQkSZIMxiWp6SLKvOixY0uv8nHHwZVXtt+/\n5ppybLxxCco/9am+Dcoz4b772oef33lnuVbPfPPBppu2B+AjRjS3rpIkSf2Fwbgk9aGPfASuuAL+\n7/9g3Di4+GKYMaPcu/XWEtCuvXYJynfYAQYPbk69Xn0VrruufeuxSZM6T/v+97cH35ttVvYClyRJ\nUtcMxiWpBay9Nlx0EXz/+2X19QsuKPOxoQTqn/scrLwyHH44fOELMO+8vV+HRx9t7/2++eb253c0\naFB5idAWgK+1VmsOp5ckSWplrl0rSS1kpZXgnHPg8cfhwANn7mV+7LGyHdqKK8Kpp5atw2bHm2/C\ntdfC175WAv1VV4VvfANuuOHdgfhii5WXABddBC+8ALfcUl4MjBxpIC5JktQT9oxLUgtadlk4+eQy\nPP3UU+G009oXSnv66RJAf//78PWvw777wnve01i5EyeWVc+vuqosIjdtWudpR41q7/1ef/3mDZGX\nJEmaGxiMS1ILe+97S9B9yCFw1lnwox/B88+Xey+8AEccAccfD/vtV3rSl1hi5vzvvFMWXGsbfn7/\n/Z0/a6GFyqJyW2xRjmWWmXPflyRJ0tzOYFyS+oGFF4bDDoMDDoDzz4cTToAnnyz3XnmlrMh+8smw\nxx6w557wwAMl+P7zn2Hy5M7LXWml9t7vj32srIYuSZKkOc9gXJL6kQUWKL3ge+5Z5m8fdxw8/HC5\n98YbZTj7aad1nn+eeUrQ3RaAr7JKc+otSZKkmRmMS1I/NM88sOuuZVG1yy6DY46Be++tn3bppduH\nno8dC8OGNbeukiRJejeDcUnqxwYNgu23h+22K/uCjxsH99wDq6/e3vs9alRJJ0mSpNZhMC5JA0AE\nfOIT5ZAkSVLrs69EkiRJkqQmG7DBeESsGhFfi4hfRcTDETEjIjIiPtNFnguqNJ0dD3eRd1BE7BcR\nd0fE1IiYEhG3RMROXeSZUJW7SSf3R0bEpCrN7yJi3m79ECRJkiRJLWkgD1PfB/haD/PeCvyrzvVJ\n9RJHxGDg98A2wCvAtcB8wBjg1xGxUWYe0J0KRMQGwNXAcOBnwB6Z+U53ypAkSZIktaaBHIz/AzgB\nuBu4BzgPGN1g3nMz84JuPOtASiD+ELBZZj4HEBErA7cA+0fEDZl5eSOFRcRmwOXAUODHwNczM7tR\nH0mSJElSCxuwwXhmnlt7HhFz5DlVr/ih1ek+bYF4VYfHIuIw4ALgCEqAPavytgF+C8wPfDczj+7t\nOkuSJEmS+taAnTPeRBsBSwBPZ+bNde5fArwFrBcRy3RVUETsAlxKGeJ+oIG4JEmSJA1MA7ZnfDZt\nGhEfpAwTfw74K3BdZs6ok/ZD1edd9QrKzNci4kFgVHVMrJcuIvYBTgcS2D0zfzZ734IkSZIkqVUZ\njNe3a51rD0XE5zPzgQ7XR1SfT3ZR3lOUQHxEJ/cPBLYFpgM7ZebvG6lkRHwJ+FIjaavnS5IkSZJa\ngMH4zO6nLPZ2AyW4XhhYBzgGWBu4PiLWycza3u2h1ee0LsqdWn0O6+T+ttXnKY0G4pXlaXxROkmS\nJElSizAYr5GZp3S4NA24KiKuA24CNgS+CXy1Jk3bynCzs9r5zcDHgIMi4p7MvLjBfBOqejViFLBI\nD+omSZIkSeplBuMNyMzpEXEcZTX0LTrcfrX6HErn2u692sn9o4CtgIMo+5LTSEBebb92wazSAUTE\neOxFlyRJkqSW4GrqjXu4+uy4IvqE6nO5LvK+v0Pad8nMg4GTgMHAhRHx2e5XUZIkSZLUHxiMN26x\n6nNqh+v3Vp/r1csUEQsCa1Wn93X1gJqAfAglIN+xZ1WVJEmSJLUyg/HGtfVUd9zC7DbgeWDZiPhY\nnXw7AvMAd3VY+K2uDgH5rw3IJUmSJGngMRivRMSoiNgqIgZ3uD4kIr4BHFBdOrn2fma+A5xQnZ4V\nEUvU5F0ZGFedHtNoXeoE5J/p1jcjSZIkSWppA3YBt4hYBziz5tIa1eexEXFw28XM3LD6cnngD8CL\nEfEo8DRlK7KRwPuAGcBhmXlNncedTFkNfWvgsYi4gdIb/nFgfuC0zLy8O/XPzIMjAsqibhdVi7r9\nrjtlSJIkSZJaU2TOzo5crSsiNgH+Mqt0mRlV+hHA14D1KYuxLUbZruxp4BbgjMy8p4vnDQL2Bb4M\nrAa8A/wdODMzf91JngnVszbNzPGdpDmREpC/DezU04A8Ip4GlllkkUUYNWpUT4qQJEmSpH7r/vvv\nZ8qUKQATM3PZvq7PgA3GNbOIeA1YoK/rIUmSJEl97PXMXLCvKzFgh6nrXd6pPqdTFp2TWskoYBFg\nCnB/H9dFqmXbVCuzfapV2TbVqjYC5qU9NupTBuNzj3uA0cBtmblJH9dFmklEjKe0z/ttn2oltk21\nMtunWpVtU62qpm12Ov24mVxNXZIkSZKkJjMYlyRJkiSpyQzGJUmSJElqMoNxSZIkSZKazGBckiRJ\nkqQmMxiXJEmSJKnJDMYlSZIkSWoyg3FJkiRJkprMYFySJEmSpCYb0tcVUNNcAIwHJvRpLaT6LsD2\nqdZ0AbZNta4LsH2qNV2AbVOt6QJaqG1GZvZ1HSRJkiRJmqs4TF2SJEmSpCYzGJckSZIkqckMxiVJ\nkiRJajKD8RYQEfNExJiIOCkibo+ISRExPSImRsTvImKTWeTfOSJuiYgpETE1Iu6OiP0iosv/vhHx\nyYi4NiJejIjXIuIfEXFERMzXRZ4FIuLw6hmvVvV8JiJ+HxGje/gjUItqdtuMiPdHxD4RcV5E/D0i\n3o6IjIiDG6xvt9u0+q/+0D4jYlBEfCQiflA96+mqjs9FxNUR8enZ/DGoBfWHttnFs/es8mZEnN7d\n/Gp9/a19RsTgiNgrIm6OiMkR8UZE/CciroiIrXvwI1CL6k9tMyIWjYhjI+KBiJgWEW9GxJMR8cuI\nGNXwN52ZHn18AB8HsjomAVcCvwUeqLn+vU7ynlHdf73K9wfglera74HBneQ7tErzNnA9cAnwfHXt\nNmDBOnkWAe6v0rwC/Bm4uEM9v9bXP0+P/ts2gQNryq09Dm6grt1u0x79++gP7RNYqSbdZOAa4DfA\nnTXXf0a1oKrHwDj6Q9vs5NnLVc+aUeU/va9/lh69f/Sn9gkMB+6o0r8MXFX9Dr0VeA04t69/nh5z\nX9sEPgA8WaX9b/W83wH/qq69BezQ0Pfc1z90jwTYrPoP+NE69z5HCS4S2LTDvR1qGuvKNdeXBB6i\nk+AYWLf6QzsN2KDm+lDgpirfyXXyHVvduwcY3uHe7tW96cCSff0z9ei3bXNb4BTgi8DqwC8a/KXY\nozbt0b+P/tA+gRWBG4BPdvyHADAamFqV8eW+/nl69N7RH9pmnTKC8iJzKmXrH4PxAXr0l/ZJGcF7\na5X2p8BCHe4PBdbq65+nR+8d/aht/rpKdxU1nT1Vmz26uvcCMM8sv+e+/qF7NNQwz63+o57X4frd\n1fVd6+QZXdMoB3W497vq3pF18q0AvAO8Cbynw73bqnyf76Sej1b3t+jrn5lHc47ebpt10rb9g3BW\nvxR71KY9BvbRKu1zFmV8uyrjhr7+eXk072jFtgnsU+XZv+Yfkwbjc+HRKu0T2KtKNx5HD3lkS7XN\nSVW6DevcG0wZtZHAGrP6npwz3j/cV30u23YhIpYFPkzpib6kY4bMvAmYCCwFbFiTb17gU9XphXXy\nPU4JuucFtuhw+80G6/tCg+nU//Va2+yp2WzTGtj6vH32pI6aK7RU24yIEcAPKb2QzhNXq7TPr1af\nx2cV5Wiu1ypts9diIoPx/mHl6nNSzbUPVZ8PZubrneS7q0NagFWBBYEXM/Pf3cgHZY44wCERMbz2\nRkR8parnfZS3U5o79Gbb7KnZadMa2Fqhfc5KvTpq4GuZthkRAZwPDAF2N+gRLdA+I2IpYC3K3Nu/\nRMTIiDg6In5SLZo1dnafoX6pz9tmpS0m+nZELNh2sfp9eiSwAPDHzHx+VgUN6aUKaQ6pfhl9qTq9\ntObWiOrzyS6yP9Uhbe3XT9G5evmgzKn4H2BLYEJE3EpZGGGN6ria8od8Rhdla4CYA22zp2anTWuA\naqH22anqD/gB1emlXaXVwNGCbfOrwCbA4Zn5SC+Wq36ohdrnB6vPCcB3gG9S1jVo882IuJmySJYj\nMucCLdQ2oUwx+xAlJnoyIm6n9JavTVkI81fAvo0UZM94C4uIIZT/mItQ5hNeUXN7aPU5rYsiplaf\nw3ohH5n5BvBpylC2oZQFiT5LeXM5CfgLZbVgDXBzqG32VLOfpxbXYu2zK2dS/mHwEHDOHH6WWkCr\ntc2IWBE4jrIw64m9Uab6rxZrn20jMEcA3wJ+SVlga2HKIl//BD5G2dVHA1yLtU2qF0CbAT8HFge2\noiwitxLwOHBTZr7aSFkG463tbGAM8B/gCx3utb0d7O5wsp7mIyKWBv4K7A18jbKs/zBgI+Bh4ATg\n6ogY3N2y1e/MibbZU81+nlpfK7XPuiLiO8BuwBTgs5nZ6Pwz9W8t0zZrhqfPC3wlM99pxnPV0lqm\nfdIeowyhBF+7ZebDmflqZv4F+ARlC6tNI2J0k+qkvtNKbZOIWI0yNXdzykrsSwPvqeo4DfhpRJzf\nSFkG4y0qIn5M2S7sWWBMZj7bIUnb25ahdK7tXu2bmZ7mg/L2ZwNgr8w8LTP/k5lTM/N2ysJY/6Ds\nD7hrF2Wrn5uDbbOnmv08tbAWbJ/vEhHfAL5HeVP/qcx8cE48R62lBdvmAZSexeMy8++9UJ76sRZs\nn7VlvGvkUGY+TdlWCkoApAGq1dpm1Ut/KaUXfPvM/FVmPpuZUzLzRmAs8Bzw5YjYdFblGYy3oIg4\nifJH8r+URvdYnWQTqs/luijq/R3S1n79ge7ki4hlKI1rOmUbqZlkZu31j3dRtvqxOdw2e6qtjG61\naQ08Ldo+ZxIR+wMnUXp0tsrM23r7GWo9Ldo2t6s+x0bE+NqD9nmZ21XXruyF56lFtWj7rC3jiU7S\ntF1fqheepxbUom1zA8paWU/U+xuemS8Cf6pOZxkTuYBbi4mIHwLfoMy9HpuZD/h2bN4AAAudSURB\nVHWStG1p/zUjYoFOVg9cr0NaKMPJXweGR8SKnaw+vX6dfG2BzrTMfLuTOr1cfQ7v5L76sSa0zZ7q\naZvWANLC7bO2jvsBpwJvANtU261ogOsHbXOjLu69rzqm9OLz1EJauH0+TBnuuxCwWCdpFq8+p3Zy\nX/1YC7fNtpioq9+LDcdE9oy3kIgYBxwCvERpdP/XWdrM/A9wL2Wu1451yhpN2YPvWcoey235ptP+\ntmaXOvlWoPxhnk778B+AZ6rPRSNilU6q1fYHvbM3mOqnmtE2e2o22rQGiFZunzXl7g2cRllt9dOZ\neX1vla3W1cptMzM3ycyodwDfrZKdUV17z+w+T62nxdvnW0DbiIx3DUOPiHko0yzALXUHnFZum7TH\nRKtFRGe/G9v2M59lTGQw3iIi4vvAYZQ3KWMzs5E3N8dVn8dHxEo1ZS1BWaUXYFydrcbGURY5OCwi\n1q/JN5SymMsg4MzMbHurQ2Y+Sfsvu/MjYskO9d+NsrI6wG8aqLv6iSa3zZ7qdpvWwNAf2mdE7FGV\nO50yv+ya3ihXra0/tE3NvfpJ+zwOmAHsFxH/PyCvFgo+HlgRmAj8oZeepxbQD9rmbZSAfAHgvIhY\nuOZ5gyLi25Rg/G0a2LY0Ml2AuK9FxDbA5dXp3UBni/k8nJnjOuQ9E9iHMuzxeuAtyhvEhYHLgM/U\nWyE1Ig6l/CJ7B7iR0uBHA0sAdwCbZeZrHfJ8kLJ92XDKAgh3Ut5YrUnZbgLgh5l5WKPfu1pbs9tm\ntWJ/7R/VFSnD0J6ibJ/XZrvMnNQhb7fbtPq3/tA+I2IU5Y19UIZd3tFJHV/IzIM7/WbVr/SHtjmL\n+h8NHEXpGf/qrNKrf+lP7bNaZ+PHlBfudwFPU/Z3XoEyTPhTrr0xcPSXthkRY6t6LkAZRn8XZcrk\nKMp2fDOAAzLzjFl+05np0ccHZaGUbOAY30n+nYFbgVco82vuAfYDBs3iuZ8ErqME1K9TGvwRwHxd\n5FmasoXZA9Wz3qoa6+XAJ/v6Z+nRu0ez2yawfIPPW76T/N1u0x799+gP7RPYpME8E/r65+kxd7XN\nWdT/6Cr96X39s/To/aO/tc/q9+iVwAuUEUZPAj9ptD179J+jP7VNYGXgLOARyr85p1OC+IuADRv9\nnu0ZlyRJkiSpyZwzLkmSJElSkxmMS5IkSZLUZAbjkiRJkiQ1mcG4JEmSJElNZjAuSZIkSVKTGYxL\nkiRJktRkBuOSJEmSJDWZwbgkSZIkSU1mMC5JkiRJUpMZjEuSJEmS1GQG45IkSZIkNZnBuCRJkiRJ\nTWYwLkmSJElSkxmMS5IkSZLUZAbjkiRJkiQ1mcG4JEmSJElNZjAuSZIkSVKTGYxLkiRJktRkBuOS\nJEmSJDWZwbgkSZIkSU1mMC5JkiRJUpMZjEuSJEmS1GQG45IkSZIkNZnBuCRJkiRJTWYwLklSPxYR\n10VE1hxHzUZZi0fEf2vKmhERH+3N+kqSpMJgXJKk/m0PYFrN+RERMbKHZZ0KLF5zfmZm3tLjmkmS\npE4ZjEuS1I9l5gTgmzWX5gF+FhGDu1NORGwN7FRzaQJw+OzWT5Ik1WcwLklS/3c6UNuD/WHgkEYz\nR8QiwFkdLu+RmVN7oW6SJKkOg3FJkvq5zExgd+D1mstHRcRqDRZxIrBMzfl5mXl9b9VPkiS9W5S/\n35Ikqb+LiEOAH9Zcug34n8yc0UWeMUBt4D0RWDMzp8yZWkqSJLBnXJKkgeRHwJ015xsBX+sscUQs\nCPy0w+W9DcQlSZrz7BmXJGkAiYg1gXuBeatLrwEfzMx/10l7CjMH6xdm5hd68MxVgA8BSwBDgcnA\nU8DNmflad8urKXdBYE1gNeC9wALAK8ALwD2Z+WhPy+7imasDawFLU76XiZn5895+jiRJBuOSJA0w\nEfEd4Hs1l8YDm2XNH/2I2BC4lfZRcs8Da2Tm5AafsQBwIGVrtRGdJHsTuBz4Vr2XAZ2UuwzweWAb\nYEPaXyrUM5GyHduZjS42FxH/oAT4UAL6davrO1MWvRvVIcs7mTmkkbIlSeoOg3FJkgaYiBgC3MXM\ngeW+mXlWdX8+4D5g9Zr7O2bm7xos/yPAxcy86FtXpgN7NtLDHBGvUXrAu+NhYJvMfKyB8mcKxikB\n/7nAbp1kMRiXJM0RBuOSJA1AEfEhyvzxtkDyVWCtzHwqIn4AHFGT/NLM/EyD5W5NCcTn73DrYeBR\nYCpluPoGwLAOafbMzI5z1DuW/wYwX3WawJNVuS8D7wCLA2tXz6j1H2DtzHxpFuV3DMb/Ahxc87z/\nA56onjWCMsS/q955SZJ6xGBckqQBKiKOAb5Vc+la4DBKr3lbkP4iZXj6cw2UtzJlPvrQmsvnAcdk\n5hMd0s4L7A0cT3vg/gawXmb+o4tnTKvqeTHw53rBdUQEsClwArBOza1fZGZnPdxteWuD8WnAQtXX\nvwMOycwJHdIv3/GaJEm9wWBckqQBqhqOfi+wRs3lycBiNedfzMxfNVje7ZQebyi9yF+e1dDziPgo\nZeu0tt7lqzNzyy7SL5eZTzZYn/mBqymBOcBbwPKZ+UwXeWqD8TanZ+b+jTxTkqTe4tZmkiQNUJn5\nJvAVoHaf8dpA/KpuBOJjaA/EAU5tZA54Zt4CjKu59KmIWLWL9A0F4lXaNygLyLX1LMwDbN9o/soj\nwEHdzCNJ0mwzGJckaQDLzDuAU+rcmgLs1Y2i9q35+i3g+93IexrtLwQC+FQ38napWqW9dtj7Bp2l\n7cSpmTm9t+ojSVKjDMYlSRr4vk3Z97vWEZk5sZHM1RztTWoujW90CzSAzHwB+GfNpY0bzVtTh/ki\n4r0RsVxELF97ALXzylfrZtFXdLcukiT1BrfqkCRpgMvM1yPiCeADNZcf7EYRqwPDa86fqoLg7ni5\n5usVZpU4IkYAOwNjgJGUVdQbsWg36vRSZv6nG+klSeo1BuOSJGlWlu1wvnt19NTwzm5ExDDKHPO9\n6dkIvoW7kfb5HpQvSVKvMBiXJEmz0mnw3END612sAvFrgQ1no+zuBPBTZ+M5kiTNFueMS5KkWZmn\nl8uLTq4fy8yB+HTg18CXgA8DS1H2BR+cmdF2AFf1cv0kSZrj7BmXJEmz8mKH8/0z8/TefEBELA7s\nU3PpBWCzzHyggezDerMukiQ1gz3jkiRpVp7rcL7yHHjG5sDgmvNvNxiIQ+kxlySpXzEYlyRJs/J3\n4LWa8zFz4BkrdTi/ppFMETGcOfNyQJKkOcpgXJIkdSkzpwO31FxaMyLW6+XHLNLh/JUG8+1M53PQ\nJUlqWQbjkiSpEWd3OD8pInpz7ZmXO5yvMqsMEbEIcGgv1kGSpKYxGJckSY24HLiv5vyjwLkRMW+j\nBUTE4IjYMSIWqnO74/zwb8yirPmAC4H3N/p8SZJaicG4JEmapcxMypDw2uHjuwF3RsR2nfWSVwH4\nuhFxDPBv4GJggTpJrwem1JzvGBFnRcR76pS5IXAzsGV16YVuf0OSJPUxtzaTJEkNycyHI2JH4FJg\naHV5beD3wLSIuBd4HngTWBhYGlgTmL+Bsl+NiGOB42su7w3sGhG3A5MoW5iNBEbUpDmb0ju+JZIk\n9SMG45IkqWGZeW3VM30JsHrNrYUoQ9dnZRrwVif3TqAE77vWXFsQ2KyT9L8C9gcua+C5kiS1FIep\nS5KkbsnMByk91F8A7gRmzCLLK8Afga8AS2XmlHqJstgN2At4uovy7gJ2zswvZubb3a2/JEmtIMoU\nMEmSpJ6JiEWBjYD3AYtRXva/CjwD/BN4NDPf6WaZQ4B1KcPgF6vKmwTcn5n/6r3aS5LUNwzGJUmS\nJElqMoepS5IkSZLUZAbjkiRJkiQ1mcG4JEmSJElNZjAuSZIkSVKTGYxLkiRJktRkBuOSJEmSJDWZ\nwbgkSZIkSU1mMC5JkiRJUpMZjEuSJEmS1GQG45IkSZIkNZnBuCRJkiRJTWYwLkmSJElSkxmMS5Ik\nSZLUZP8Pq3no5Glk2bgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0423542b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=df_US.plot(kind='line', x=\"Year\", figsize=(15,8), color='blue')\n",
    "ax.yaxis.set_major_formatter(tick.FuncFormatter(fmt_x))\n",
    "plt.xlabel(\"Year\", labelpad=20, fontsize = 40)\n",
    "plt.ylabel(\"Median Sale Price (in $)\", labelpad=20)\n",
    "plt.title(\"Median Sale Price growth over years (2008 - 2018) in U.S\")\n",
    "ax.legend_.remove()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insights : Median Sale price of houses in U.S increased by 55% from its rock bottom in 2011. The decrease from 2008 to 2011 can be explained by the depression in the ecomony and hence the housing market. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the prices seen on the y-axis, 140k - 230k, is a scale for prices unheard of in California. we see this discrepancy, due the inclusion of all states (with Housing prices high and low ranges). It could also be due the types of houses (condos, townhomes, single-family homes) and in all zipcodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Hence we like to investigate further to see how the various states fared in terms of the median house price increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Median Sale Price Increase By State <a class=\"anchor\" id=\"plot2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2008-03</th>\n",
       "      <th>2008-04</th>\n",
       "      <th>2008-05</th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-07</th>\n",
       "      <th>2008-08</th>\n",
       "      <th>2008-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>387800.0</td>\n",
       "      <td>370900.0</td>\n",
       "      <td>348000.0</td>\n",
       "      <td>332700.0</td>\n",
       "      <td>317800.0</td>\n",
       "      <td>305500.0</td>\n",
       "      <td>291500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>466200.0</td>\n",
       "      <td>471600.0</td>\n",
       "      <td>472600.0</td>\n",
       "      <td>475000.0</td>\n",
       "      <td>479100.0</td>\n",
       "      <td>483100.0</td>\n",
       "      <td>487800.0</td>\n",
       "      <td>487100.0</td>\n",
       "      <td>487200.0</td>\n",
       "      <td>485800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>212400.0</td>\n",
       "      <td>211600.0</td>\n",
       "      <td>211300.0</td>\n",
       "      <td>213400.0</td>\n",
       "      <td>215200.0</td>\n",
       "      <td>216300.0</td>\n",
       "      <td>216900.0</td>\n",
       "      <td>218100.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>New York</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>286700.0</td>\n",
       "      <td>295300.0</td>\n",
       "      <td>303000.0</td>\n",
       "      <td>308000.0</td>\n",
       "      <td>305100.0</td>\n",
       "      <td>298600.0</td>\n",
       "      <td>295600.0</td>\n",
       "      <td>288100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID  RegionName  SizeRank   2008-03   2008-04   2008-05   2008-06  \\\n",
       "0         9  California         1  387800.0  370900.0  348000.0  332700.0   \n",
       "1        54       Texas         2       NaN       NaN       NaN       NaN   \n",
       "2        43    New York         3       NaN       NaN       NaN       NaN   \n",
       "\n",
       "    2008-07   2008-08   2008-09    ...      2017-10   2017-11   2017-12  \\\n",
       "0  317800.0  305500.0  291500.0    ...     466200.0  471600.0  472600.0   \n",
       "1       NaN       NaN       NaN    ...     213000.0  212400.0  211600.0   \n",
       "2       NaN       NaN       NaN    ...     286700.0  295300.0  303000.0   \n",
       "\n",
       "    2018-01   2018-02   2018-03   2018-04   2018-05   2018-06   2018-07  \n",
       "0  475000.0  479100.0  483100.0  487800.0  487100.0  487200.0  485800.0  \n",
       "1  211300.0  213400.0  215200.0  216300.0  216900.0  218100.0       NaN  \n",
       "2  308000.0  305100.0  298600.0  295600.0  288100.0       NaN       NaN  \n",
       "\n",
       "[3 rows x 128 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SalePrice_State = pd.read_csv(\"Median Price\\Sale_Prices_State.CSV\")\n",
    "df_SalePrice_State.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2008-07</th>\n",
       "      <th>2009-01</th>\n",
       "      <th>2009-07</th>\n",
       "      <th>2010-01</th>\n",
       "      <th>2010-07</th>\n",
       "      <th>2011-01</th>\n",
       "      <th>2011-07</th>\n",
       "      <th>2012-01</th>\n",
       "      <th>2012-07</th>\n",
       "      <th>2013-01</th>\n",
       "      <th>...</th>\n",
       "      <th>2014-01</th>\n",
       "      <th>2014-07</th>\n",
       "      <th>2015-01</th>\n",
       "      <th>2015-07</th>\n",
       "      <th>2016-01</th>\n",
       "      <th>2016-07</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-07</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegionName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>317800.0</td>\n",
       "      <td>243000.0</td>\n",
       "      <td>230600.0</td>\n",
       "      <td>263000.0</td>\n",
       "      <td>266800.0</td>\n",
       "      <td>255400.0</td>\n",
       "      <td>243900.0</td>\n",
       "      <td>246600.0</td>\n",
       "      <td>271600.0</td>\n",
       "      <td>309600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>369200.0</td>\n",
       "      <td>379700.0</td>\n",
       "      <td>387000.0</td>\n",
       "      <td>401900.0</td>\n",
       "      <td>418900.0</td>\n",
       "      <td>422600.0</td>\n",
       "      <td>439100.0</td>\n",
       "      <td>459700.0</td>\n",
       "      <td>475000.0</td>\n",
       "      <td>485800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173700.0</td>\n",
       "      <td>179800.0</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>187200.0</td>\n",
       "      <td>194200.0</td>\n",
       "      <td>200400.0</td>\n",
       "      <td>204600.0</td>\n",
       "      <td>211300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>246800.0</td>\n",
       "      <td>258000.0</td>\n",
       "      <td>226000.0</td>\n",
       "      <td>247600.0</td>\n",
       "      <td>252300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>263800.0</td>\n",
       "      <td>261500.0</td>\n",
       "      <td>260100.0</td>\n",
       "      <td>262700.0</td>\n",
       "      <td>267900.0</td>\n",
       "      <td>280400.0</td>\n",
       "      <td>279300.0</td>\n",
       "      <td>291600.0</td>\n",
       "      <td>308000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>184300.0</td>\n",
       "      <td>150900.0</td>\n",
       "      <td>126100.0</td>\n",
       "      <td>130300.0</td>\n",
       "      <td>120600.0</td>\n",
       "      <td>115100.0</td>\n",
       "      <td>116000.0</td>\n",
       "      <td>120400.0</td>\n",
       "      <td>125700.0</td>\n",
       "      <td>135900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151600.0</td>\n",
       "      <td>154100.0</td>\n",
       "      <td>161800.0</td>\n",
       "      <td>171700.0</td>\n",
       "      <td>180200.0</td>\n",
       "      <td>187000.0</td>\n",
       "      <td>195600.0</td>\n",
       "      <td>203000.0</td>\n",
       "      <td>220500.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>181100.0</td>\n",
       "      <td>174200.0</td>\n",
       "      <td>152100.0</td>\n",
       "      <td>171500.0</td>\n",
       "      <td>158800.0</td>\n",
       "      <td>156200.0</td>\n",
       "      <td>141000.0</td>\n",
       "      <td>138700.0</td>\n",
       "      <td>140100.0</td>\n",
       "      <td>143800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>169600.0</td>\n",
       "      <td>168500.0</td>\n",
       "      <td>176000.0</td>\n",
       "      <td>181800.0</td>\n",
       "      <td>178500.0</td>\n",
       "      <td>185400.0</td>\n",
       "      <td>190500.0</td>\n",
       "      <td>192400.0</td>\n",
       "      <td>194600.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             2008-07   2009-01   2009-07   2010-01   2010-07   2011-01  \\\n",
       "RegionName                                                               \n",
       "California  317800.0  243000.0  230600.0  263000.0  266800.0  255400.0   \n",
       "Texas            NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "New York         NaN       NaN       NaN       NaN       NaN  246800.0   \n",
       "Florida     184300.0  150900.0  126100.0  130300.0  120600.0  115100.0   \n",
       "Illinois    181100.0  174200.0  152100.0  171500.0  158800.0  156200.0   \n",
       "\n",
       "             2011-07   2012-01   2012-07   2013-01    ...      2014-01  \\\n",
       "RegionName                                            ...                \n",
       "California  243900.0  246600.0  271600.0  309600.0    ...     369200.0   \n",
       "Texas            NaN       NaN       NaN       NaN    ...          NaN   \n",
       "New York    258000.0  226000.0  247600.0  252300.0    ...     263800.0   \n",
       "Florida     116000.0  120400.0  125700.0  135900.0    ...     151600.0   \n",
       "Illinois    141000.0  138700.0  140100.0  143800.0    ...     169600.0   \n",
       "\n",
       "             2014-07   2015-01   2015-07   2016-01   2016-07   2017-01  \\\n",
       "RegionName                                                               \n",
       "California  379700.0  387000.0  401900.0  418900.0  422600.0  439100.0   \n",
       "Texas       173700.0  179800.0  186000.0  187200.0  194200.0  200400.0   \n",
       "New York    261500.0  260100.0  262700.0  267900.0  280400.0  279300.0   \n",
       "Florida     154100.0  161800.0  171700.0  180200.0  187000.0  195600.0   \n",
       "Illinois    168500.0  176000.0  181800.0  178500.0  185400.0  190500.0   \n",
       "\n",
       "             2017-07   2018-01   2018-07  \n",
       "RegionName                                \n",
       "California  459700.0  475000.0  485800.0  \n",
       "Texas       204600.0  211300.0       NaN  \n",
       "New York    291600.0  308000.0       NaN  \n",
       "Florida     203000.0  220500.0       NaN  \n",
       "Illinois    192400.0  194600.0       NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state = df_SalePrice_State.iloc[0:,1::6]\n",
    "\n",
    "df_state.set_index('RegionName').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RegionName</th>\n",
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       "      <th>38</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>297600.0</td>\n",
       "      <td>287700.0</td>\n",
       "      <td>291400.0</td>\n",
       "      <td>284300.0</td>\n",
       "      <td>306900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284300.0</td>\n",
       "      <td>306900.0</td>\n",
       "      <td>22600.0</td>\n",
       "      <td>7.949349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>236300.0</td>\n",
       "      <td>231700.0</td>\n",
       "      <td>215600.0</td>\n",
       "      <td>238000.0</td>\n",
       "      <td>223800.0</td>\n",
       "      <td>253900.0</td>\n",
       "      <td>229200.0</td>\n",
       "      <td>224400.0</td>\n",
       "      <td>226400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>220200.0</td>\n",
       "      <td>221600.0</td>\n",
       "      <td>229100.0</td>\n",
       "      <td>225700.0</td>\n",
       "      <td>241100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215600.0</td>\n",
       "      <td>253900.0</td>\n",
       "      <td>38300.0</td>\n",
       "      <td>17.764378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>305100.0</td>\n",
       "      <td>292000.0</td>\n",
       "      <td>273900.0</td>\n",
       "      <td>272100.0</td>\n",
       "      <td>274900.0</td>\n",
       "      <td>274600.0</td>\n",
       "      <td>269100.0</td>\n",
       "      <td>257000.0</td>\n",
       "      <td>266500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>276100.0</td>\n",
       "      <td>276000.0</td>\n",
       "      <td>280100.0</td>\n",
       "      <td>271600.0</td>\n",
       "      <td>276800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257000.0</td>\n",
       "      <td>305100.0</td>\n",
       "      <td>48100.0</td>\n",
       "      <td>18.715953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Texas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>187200.0</td>\n",
       "      <td>194200.0</td>\n",
       "      <td>200400.0</td>\n",
       "      <td>204600.0</td>\n",
       "      <td>211300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173700.0</td>\n",
       "      <td>211300.0</td>\n",
       "      <td>37600.0</td>\n",
       "      <td>21.646517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>232900.0</td>\n",
       "      <td>218200.0</td>\n",
       "      <td>215200.0</td>\n",
       "      <td>227600.0</td>\n",
       "      <td>220900.0</td>\n",
       "      <td>225600.0</td>\n",
       "      <td>213900.0</td>\n",
       "      <td>213200.0</td>\n",
       "      <td>230800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>244200.0</td>\n",
       "      <td>248300.0</td>\n",
       "      <td>252700.0</td>\n",
       "      <td>257200.0</td>\n",
       "      <td>259700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213200.0</td>\n",
       "      <td>259700.0</td>\n",
       "      <td>46500.0</td>\n",
       "      <td>21.810507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RegionName   2008-07   2009-01   2009-07   2010-01   2010-07   2011-01  \\\n",
       "38       Alaska       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "28  Connecticut  236300.0  231700.0  215600.0  238000.0  223800.0  253900.0   \n",
       "10   New Jersey  305100.0  292000.0  273900.0  272100.0  274900.0  274600.0   \n",
       "1         Texas       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "11     Virginia  232900.0  218200.0  215200.0  227600.0  220900.0  225600.0   \n",
       "\n",
       "     2011-07   2012-01   2012-07    ...       2016-01   2016-07   2017-01  \\\n",
       "38       NaN       NaN       NaN    ...      297600.0  287700.0  291400.0   \n",
       "28  229200.0  224400.0  226400.0    ...      220200.0  221600.0  229100.0   \n",
       "10  269100.0  257000.0  266500.0    ...      276100.0  276000.0  280100.0   \n",
       "1        NaN       NaN       NaN    ...      187200.0  194200.0  200400.0   \n",
       "11  213900.0  213200.0  230800.0    ...      244200.0  248300.0  252700.0   \n",
       "\n",
       "     2017-07   2018-01  2018-07       Min       Max     Diff  PctChange  \n",
       "38  284300.0  306900.0      NaN  284300.0  306900.0  22600.0   7.949349  \n",
       "28  225700.0  241100.0      NaN  215600.0  253900.0  38300.0  17.764378  \n",
       "10  271600.0  276800.0      NaN  257000.0  305100.0  48100.0  18.715953  \n",
       "1   204600.0  211300.0      NaN  173700.0  211300.0  37600.0  21.646517  \n",
       "11  257200.0  259700.0      NaN  213200.0  259700.0  46500.0  21.810507  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Calculate Min and Max of the series\n",
    "df_state['Min'] = df_state.min(axis=1)\n",
    "df_state['Max'] = df_state.max(axis=1)\n",
    "\n",
    "##Calculate Diff of Min and Max\n",
    "df_state['Diff'] = df_state['Max'] - df_state['Min']\n",
    "\n",
    "##Calculate Percentage increase from the rock bottom in 2011\n",
    "df_state['PctChange'] = (df_state['Diff']/df_state['Min'])*100\n",
    "\n",
    "## Sort Values by percentage increase\n",
    "df_state.sort_values(by='PctChange').head()\n",
    "\n",
    "df_top10 = df_state.sort_values(by='PctChange').tail(10)\n",
    "df_top10 = df_top10.ix[:, ['RegionName','PctChange']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PctChange</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegionName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>63.805970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>66.920566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>75.013492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>87.837838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>91.572546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>91.716621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>110.667823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>110.880829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>128.381963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>178.842975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                PctChange\n",
       "RegionName               \n",
       "Tennessee       63.805970\n",
       "West Virginia   66.920566\n",
       "Oregon          75.013492\n",
       "Georgia         87.837838\n",
       "Florida         91.572546\n",
       "Colorado        91.716621\n",
       "California     110.667823\n",
       "Arizona        110.880829\n",
       "Nevada         128.381963\n",
       "Michigan       178.842975"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top10.set_index('RegionName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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D0gSdFhDPHqEOF9U8jx4hz1BBkAnK2KuWcXmPE8WEQRDKSfaymu7hfdLsSbkIvo7VW6iM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0423e15c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"poster\")\n",
    "ax = df_top10.plot(kind='barh',x='RegionName', figsize=(15, 10), rot=0, color='Teal')\n",
    "ax.xaxis.set_major_formatter(tick.FuncFormatter(fmt_x))\n",
    "plt.xlabel(\"Percentage increase\", labelpad=20)\n",
    "plt.ylabel(\"State\", labelpad=20)\n",
    "plt.title(\"Top 10 states by percentage increase in Median Sale Price from Sept 2011\")\n",
    "ax.legend_.remove()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis2:  Michigan and Nevada had about 175% to 150% increase in Median Sale price in the last few years from Sept 2011. California came in 4th with 115% increase in meadian Sale price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I tried to delve in further more to see which metros have the high increase in median sale price, in the top 5 metros by size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 5 metros by size <a class=\"anchor\" id=\"plot3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>RegionID</th>\n",
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       "      <th>StateName</th>\n",
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       "      <th>2008-07</th>\n",
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       "      <td>552900.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>17426.0</td>\n",
       "      <td>Chicago</td>\n",
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       "      <td>3</td>\n",
       "      <td>324300.0</td>\n",
       "      <td>313100.0</td>\n",
       "      <td>291700.0</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>277400.0</td>\n",
       "      <td>273700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>267900.0</td>\n",
       "      <td>271400.0</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>280700.0</td>\n",
       "      <td>286400.0</td>\n",
       "      <td>299400.0</td>\n",
       "      <td>298500.0</td>\n",
       "      <td>293100.0</td>\n",
       "      <td>277800.0</td>\n",
       "      <td>272500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13271.0</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>4</td>\n",
       "      <td>113100.0</td>\n",
       "      <td>113200.0</td>\n",
       "      <td>113500.0</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>108600.0</td>\n",
       "      <td>112100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143700.0</td>\n",
       "      <td>146500.0</td>\n",
       "      <td>149800.0</td>\n",
       "      <td>156700.0</td>\n",
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       "      <td>154900.0</td>\n",
       "      <td>152400.0</td>\n",
       "      <td>154600.0</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40326.0</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5</td>\n",
       "      <td>220900.0</td>\n",
       "      <td>213000.0</td>\n",
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       "      <td>187700.0</td>\n",
       "      <td>177100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>223100.0</td>\n",
       "      <td>226000.0</td>\n",
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       "      <td>237900.0</td>\n",
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       "   RegionID    RegionName     StateName  SizeRank   2008-03   2008-04  \\\n",
       "0    6181.0      New York      New York         1       NaN       NaN   \n",
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       "2   17426.0       Chicago      Illinois         3  324300.0  313100.0   \n",
       "3   13271.0  Philadelphia  Pennsylvania         4  113100.0  113200.0   \n",
       "4   40326.0       Phoenix       Arizona         5  220900.0  213000.0   \n",
       "\n",
       "    2008-05   2008-06   2008-07   2008-08    ...      2017-10   2017-11  \\\n",
       "0       NaN       NaN       NaN       NaN    ...     546500.0  546000.0   \n",
       "1  459500.0  449200.0  432600.0  419400.0    ...     647500.0  663500.0   \n",
       "2  291700.0  282100.0  277400.0  273700.0    ...     267900.0  271400.0   \n",
       "3  113500.0  110300.0  108600.0  112100.0    ...     143700.0  146500.0   \n",
       "4  206600.0  198300.0  187700.0  177100.0    ...     223100.0  226000.0   \n",
       "\n",
       "    2017-12   2018-01   2018-02   2018-03   2018-04   2018-05   2018-06  \\\n",
       "0  552100.0  565700.0  572600.0  574800.0  569500.0  567100.0  556200.0   \n",
       "1  672200.0  687000.0  696900.0  704600.0  695900.0  685900.0  688000.0   \n",
       "2  275000.0  280700.0  286400.0  299400.0  298500.0  293100.0  277800.0   \n",
       "3  149800.0  156700.0  154700.0  154900.0  152400.0  154600.0  153000.0   \n",
       "4  230100.0  236800.0  239400.0  238200.0  235800.0  233700.0  237000.0   \n",
       "\n",
       "    2018-07  \n",
       "0  552900.0  \n",
       "1  684800.0  \n",
       "2  272500.0  \n",
       "3       NaN  \n",
       "4  237900.0  \n",
       "\n",
       "[5 rows x 129 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SalePrice_City = pd.read_csv(\"Median Price\\Sale_Prices_City.CSV\")\n",
    "df_SalePrice_City.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54296.0</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>California</td>\n",
       "      <td>8</td>\n",
       "      <td>419800.0</td>\n",
       "      <td>408200.0</td>\n",
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       "      <td>371300.0</td>\n",
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       "      <td>572000.0</td>\n",
       "      <td>573200.0</td>\n",
       "      <td>577600.0</td>\n",
       "      <td>583200.0</td>\n",
       "      <td>584200.0</td>\n",
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       "      <th>9</th>\n",
       "      <td>33839.0</td>\n",
       "      <td>San Jose</td>\n",
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       "   RegionID   RegionName   StateName  SizeRank   2008-03   2008-04   2008-05  \\\n",
       "1   12447.0  Los Angeles  California         2  508700.0  480000.0  459500.0   \n",
       "2   17426.0      Chicago    Illinois         3  324300.0  313100.0  291700.0   \n",
       "4   40326.0      Phoenix     Arizona         5  220900.0  213000.0  206600.0   \n",
       "7   54296.0    San Diego  California         8  419800.0  408200.0  398800.0   \n",
       "9   33839.0     San Jose  California        10  615200.0  578200.0  560700.0   \n",
       "\n",
       "    2008-06   2008-07   2008-08    ...       2017-10   2017-11   2017-12  \\\n",
       "1  449200.0  432600.0  419400.0    ...      647500.0  663500.0  672200.0   \n",
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       "7  380600.0  371300.0  356600.0    ...      553600.0  566300.0  574300.0   \n",
       "9  543300.0  529300.0  517700.0    ...      885300.0  922800.0  970900.0   \n",
       "\n",
       "     2018-01    2018-02    2018-03    2018-04    2018-05    2018-06    2018-07  \n",
       "1   687000.0   696900.0   704600.0   695900.0   685900.0   688000.0   684800.0  \n",
       "2   280700.0   286400.0   299400.0   298500.0   293100.0   277800.0   272500.0  \n",
       "4   236800.0   239400.0   238200.0   235800.0   233700.0   237000.0   237900.0  \n",
       "7   577800.0   572000.0   573200.0   577600.0   583200.0   584200.0   585000.0  \n",
       "9  1002300.0  1044300.0  1060800.0  1071700.0  1065200.0  1054600.0  1030600.0  \n",
       "\n",
       "[5 rows x 129 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SalePrice_Cities = df_SalePrice_City.sort_values(by=\"SizeRank\").head(5)\n",
    "df_SalePrice_City.dropna(axis=0, inplace=True)\n",
    "df_SalePrice_City.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>491600.0</td>\n",
       "      <td>490700.0</td>\n",
       "      <td>524600.0</td>\n",
       "      <td>540400.0</td>\n",
       "      <td>540900.0</td>\n",
       "      <td>543900.0</td>\n",
       "      <td>544000.0</td>\n",
       "      <td>552100.0</td>\n",
       "      <td>556200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>449200.0</td>\n",
       "      <td>374700.0</td>\n",
       "      <td>323100.0</td>\n",
       "      <td>354400.0</td>\n",
       "      <td>347000.0</td>\n",
       "      <td>364500.0</td>\n",
       "      <td>343600.0</td>\n",
       "      <td>333200.0</td>\n",
       "      <td>341700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>484400.0</td>\n",
       "      <td>504400.0</td>\n",
       "      <td>510900.0</td>\n",
       "      <td>532300.0</td>\n",
       "      <td>563200.0</td>\n",
       "      <td>578400.0</td>\n",
       "      <td>605600.0</td>\n",
       "      <td>629700.0</td>\n",
       "      <td>672200.0</td>\n",
       "      <td>688000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>264000.0</td>\n",
       "      <td>231200.0</td>\n",
       "      <td>242200.0</td>\n",
       "      <td>231200.0</td>\n",
       "      <td>215200.0</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>194600.0</td>\n",
       "      <td>196000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>251500.0</td>\n",
       "      <td>259300.0</td>\n",
       "      <td>268300.0</td>\n",
       "      <td>272200.0</td>\n",
       "      <td>257800.0</td>\n",
       "      <td>269800.0</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>276400.0</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>277800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>126400.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>105600.0</td>\n",
       "      <td>111300.0</td>\n",
       "      <td>103800.0</td>\n",
       "      <td>108200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>117500.0</td>\n",
       "      <td>119200.0</td>\n",
       "      <td>119600.0</td>\n",
       "      <td>123400.0</td>\n",
       "      <td>124600.0</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>131500.0</td>\n",
       "      <td>145900.0</td>\n",
       "      <td>149800.0</td>\n",
       "      <td>153000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>198300.0</td>\n",
       "      <td>122600.0</td>\n",
       "      <td>82300.0</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>84600.0</td>\n",
       "      <td>82300.0</td>\n",
       "      <td>98800.0</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>164400.0</td>\n",
       "      <td>162500.0</td>\n",
       "      <td>175100.0</td>\n",
       "      <td>190000.0</td>\n",
       "      <td>198300.0</td>\n",
       "      <td>205100.0</td>\n",
       "      <td>212400.0</td>\n",
       "      <td>214700.0</td>\n",
       "      <td>230100.0</td>\n",
       "      <td>237000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RegionName   2008-06   2008-12   2009-06   2009-12   2010-06   2010-12  \\\n",
       "0      New York       NaN       NaN       NaN       NaN       NaN  449900.0   \n",
       "1   Los Angeles  449200.0  374700.0  323100.0  354400.0  347000.0  364500.0   \n",
       "2       Chicago  282100.0  264000.0  231200.0  242200.0  231200.0  215200.0   \n",
       "3  Philadelphia  110300.0  110300.0  113600.0  126400.0  120000.0  105600.0   \n",
       "4       Phoenix  198300.0  122600.0   82300.0  105000.0  105500.0   84600.0   \n",
       "\n",
       "    2011-06   2011-12   2012-06    ...      2013-12   2014-06   2014-12  \\\n",
       "0  463500.0  438900.0  470600.0    ...     482800.0  491600.0  490700.0   \n",
       "1  343600.0  333200.0  341700.0    ...     484400.0  504400.0  510900.0   \n",
       "2  194000.0  194600.0  196000.0    ...     251500.0  259300.0  268300.0   \n",
       "3  111300.0  103800.0  108200.0    ...     117500.0  119200.0  119600.0   \n",
       "4   82300.0   98800.0  122000.0    ...     164400.0  162500.0  175100.0   \n",
       "\n",
       "    2015-06   2015-12   2016-06   2016-12   2017-06   2017-12   2018-06  \n",
       "0  524600.0  540400.0  540900.0  543900.0  544000.0  552100.0  556200.0  \n",
       "1  532300.0  563200.0  578400.0  605600.0  629700.0  672200.0  688000.0  \n",
       "2  272200.0  257800.0  269800.0  281500.0  276400.0  275000.0  277800.0  \n",
       "3  123400.0  124600.0  127900.0  131500.0  145900.0  149800.0  153000.0  \n",
       "4  190000.0  198300.0  205100.0  212400.0  214700.0  230100.0  237000.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select subset of columns\n",
    "df_Cities_6mon = df_SalePrice_Cities.iloc[0:,1::6]\n",
    "df_Cities_6mon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-12</th>\n",
       "      <th>2009-06</th>\n",
       "      <th>2009-12</th>\n",
       "      <th>2010-06</th>\n",
       "      <th>2010-12</th>\n",
       "      <th>2011-06</th>\n",
       "      <th>2011-12</th>\n",
       "      <th>2012-06</th>\n",
       "      <th>2012-12</th>\n",
       "      <th>...</th>\n",
       "      <th>2013-12</th>\n",
       "      <th>2014-06</th>\n",
       "      <th>2014-12</th>\n",
       "      <th>2015-06</th>\n",
       "      <th>2015-12</th>\n",
       "      <th>2016-06</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-06</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-06</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RegionName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>449900.0</td>\n",
       "      <td>463500.0</td>\n",
       "      <td>438900.0</td>\n",
       "      <td>470600.0</td>\n",
       "      <td>492300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>482800.0</td>\n",
       "      <td>491600.0</td>\n",
       "      <td>490700.0</td>\n",
       "      <td>524600.0</td>\n",
       "      <td>540400.0</td>\n",
       "      <td>540900.0</td>\n",
       "      <td>543900.0</td>\n",
       "      <td>544000.0</td>\n",
       "      <td>552100.0</td>\n",
       "      <td>556200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>449200.0</td>\n",
       "      <td>374700.0</td>\n",
       "      <td>323100.0</td>\n",
       "      <td>354400.0</td>\n",
       "      <td>347000.0</td>\n",
       "      <td>364500.0</td>\n",
       "      <td>343600.0</td>\n",
       "      <td>333200.0</td>\n",
       "      <td>341700.0</td>\n",
       "      <td>400400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>484400.0</td>\n",
       "      <td>504400.0</td>\n",
       "      <td>510900.0</td>\n",
       "      <td>532300.0</td>\n",
       "      <td>563200.0</td>\n",
       "      <td>578400.0</td>\n",
       "      <td>605600.0</td>\n",
       "      <td>629700.0</td>\n",
       "      <td>672200.0</td>\n",
       "      <td>688000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>282100.0</td>\n",
       "      <td>264000.0</td>\n",
       "      <td>231200.0</td>\n",
       "      <td>242200.0</td>\n",
       "      <td>231200.0</td>\n",
       "      <td>215200.0</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>194600.0</td>\n",
       "      <td>196000.0</td>\n",
       "      <td>203800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>251500.0</td>\n",
       "      <td>259300.0</td>\n",
       "      <td>268300.0</td>\n",
       "      <td>272200.0</td>\n",
       "      <td>257800.0</td>\n",
       "      <td>269800.0</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>276400.0</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>277800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia</th>\n",
       "      <td>110300.0</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>126400.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>105600.0</td>\n",
       "      <td>111300.0</td>\n",
       "      <td>103800.0</td>\n",
       "      <td>108200.0</td>\n",
       "      <td>111500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>117500.0</td>\n",
       "      <td>119200.0</td>\n",
       "      <td>119600.0</td>\n",
       "      <td>123400.0</td>\n",
       "      <td>124600.0</td>\n",
       "      <td>127900.0</td>\n",
       "      <td>131500.0</td>\n",
       "      <td>145900.0</td>\n",
       "      <td>149800.0</td>\n",
       "      <td>153000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phoenix</th>\n",
       "      <td>198300.0</td>\n",
       "      <td>122600.0</td>\n",
       "      <td>82300.0</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>84600.0</td>\n",
       "      <td>82300.0</td>\n",
       "      <td>98800.0</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>142800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>164400.0</td>\n",
       "      <td>162500.0</td>\n",
       "      <td>175100.0</td>\n",
       "      <td>190000.0</td>\n",
       "      <td>198300.0</td>\n",
       "      <td>205100.0</td>\n",
       "      <td>212400.0</td>\n",
       "      <td>214700.0</td>\n",
       "      <td>230100.0</td>\n",
       "      <td>237000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               2008-06   2008-12   2009-06   2009-12   2010-06   2010-12  \\\n",
       "RegionName                                                                 \n",
       "New York           NaN       NaN       NaN       NaN       NaN  449900.0   \n",
       "Los Angeles   449200.0  374700.0  323100.0  354400.0  347000.0  364500.0   \n",
       "Chicago       282100.0  264000.0  231200.0  242200.0  231200.0  215200.0   \n",
       "Philadelphia  110300.0  110300.0  113600.0  126400.0  120000.0  105600.0   \n",
       "Phoenix       198300.0  122600.0   82300.0  105000.0  105500.0   84600.0   \n",
       "\n",
       "               2011-06   2011-12   2012-06   2012-12    ...      2013-12  \\\n",
       "RegionName                                              ...                \n",
       "New York      463500.0  438900.0  470600.0  492300.0    ...     482800.0   \n",
       "Los Angeles   343600.0  333200.0  341700.0  400400.0    ...     484400.0   \n",
       "Chicago       194000.0  194600.0  196000.0  203800.0    ...     251500.0   \n",
       "Philadelphia  111300.0  103800.0  108200.0  111500.0    ...     117500.0   \n",
       "Phoenix        82300.0   98800.0  122000.0  142800.0    ...     164400.0   \n",
       "\n",
       "               2014-06   2014-12   2015-06   2015-12   2016-06   2016-12  \\\n",
       "RegionName                                                                 \n",
       "New York      491600.0  490700.0  524600.0  540400.0  540900.0  543900.0   \n",
       "Los Angeles   504400.0  510900.0  532300.0  563200.0  578400.0  605600.0   \n",
       "Chicago       259300.0  268300.0  272200.0  257800.0  269800.0  281500.0   \n",
       "Philadelphia  119200.0  119600.0  123400.0  124600.0  127900.0  131500.0   \n",
       "Phoenix       162500.0  175100.0  190000.0  198300.0  205100.0  212400.0   \n",
       "\n",
       "               2017-06   2017-12   2018-06  \n",
       "RegionName                                  \n",
       "New York      544000.0  552100.0  556200.0  \n",
       "Los Angeles   629700.0  672200.0  688000.0  \n",
       "Chicago       276400.0  275000.0  277800.0  \n",
       "Philadelphia  145900.0  149800.0  153000.0  \n",
       "Phoenix       214700.0  230100.0  237000.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Cities_6mon.set_index('RegionName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>RegionName</th>\n",
       "      <th>New York</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Chicago</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Phoenix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>449200.0</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>198300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>374700.0</td>\n",
       "      <td>264000.0</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>122600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>323100.0</td>\n",
       "      <td>231200.0</td>\n",
       "      <td>113600.0</td>\n",
       "      <td>82300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>354400.0</td>\n",
       "      <td>242200.0</td>\n",
       "      <td>126400.0</td>\n",
       "      <td>105000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>347000.0</td>\n",
       "      <td>231200.0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>105500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RegionName  New York  Los Angeles   Chicago  Philadelphia   Phoenix\n",
       "2008-06          NaN     449200.0  282100.0      110300.0  198300.0\n",
       "2008-12          NaN     374700.0  264000.0      110300.0  122600.0\n",
       "2009-06          NaN     323100.0  231200.0      113600.0   82300.0\n",
       "2009-12          NaN     354400.0  242200.0      126400.0  105000.0\n",
       "2010-06          NaN     347000.0  231200.0      120000.0  105500.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Transpose\n",
    "df_cities_T = df_Cities_6mon.set_index('RegionName').T\n",
    "df_cities_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>RegionName</th>\n",
       "      <th>New York</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Chicago</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Phoenix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-12</th>\n",
       "      <td>449900.0</td>\n",
       "      <td>364500.0</td>\n",
       "      <td>215200.0</td>\n",
       "      <td>105600.0</td>\n",
       "      <td>84600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>463500.0</td>\n",
       "      <td>343600.0</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>111300.0</td>\n",
       "      <td>82300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>438900.0</td>\n",
       "      <td>333200.0</td>\n",
       "      <td>194600.0</td>\n",
       "      <td>103800.0</td>\n",
       "      <td>98800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06</th>\n",
       "      <td>470600.0</td>\n",
       "      <td>341700.0</td>\n",
       "      <td>196000.0</td>\n",
       "      <td>108200.0</td>\n",
       "      <td>122000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12</th>\n",
       "      <td>492300.0</td>\n",
       "      <td>400400.0</td>\n",
       "      <td>203800.0</td>\n",
       "      <td>111500.0</td>\n",
       "      <td>142800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RegionName  New York  Los Angeles   Chicago  Philadelphia   Phoenix\n",
       "2010-12     449900.0     364500.0  215200.0      105600.0   84600.0\n",
       "2011-06     463500.0     343600.0  194000.0      111300.0   82300.0\n",
       "2011-12     438900.0     333200.0  194600.0      103800.0   98800.0\n",
       "2012-06     470600.0     341700.0  196000.0      108200.0  122000.0\n",
       "2012-12     492300.0     400400.0  203800.0      111500.0  142800.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities_T = df_cities_T.dropna()\n",
    "df_cities_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Reset index\n",
    "df_cities_T.reset_index(level=0,inplace=True)\n",
    "\n",
    "##Name columns\n",
    "df_cities_T.columns = ['YearMonth', 'New York','Los Angeles', 'Chicago', 'Philadelphia', 'Phoenix',]\n",
    "\n",
    "#Extract Month and Year into separate columns\n",
    "df_cities_T['Year']= (df_cities_T['YearMonth'].str.split('-').str[0])\n",
    "df_cities_T['Month']= (df_cities_T['YearMonth'].str.split('-').str[1])\n",
    "\n",
    "df_cities_T['MonthName']=df_cities_T['Month'].apply(lambda x: 'Jun' if x == '06' else 'Dec')\n",
    "df_cities_T['Yr']=df_cities_T['Year'].apply(lambda x: '\\''+x[-2:])\n",
    "df_cities_T['MonthYr']=df_cities_T['MonthName'] +' '+ df_cities_T['Yr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>New York</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Chicago</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Phoenix</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>Yr</th>\n",
       "      <th>MonthYr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-12</td>\n",
       "      <td>449900.0</td>\n",
       "      <td>364500.0</td>\n",
       "      <td>215200.0</td>\n",
       "      <td>105600.0</td>\n",
       "      <td>84600.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>'10</td>\n",
       "      <td>Dec '10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-06</td>\n",
       "      <td>463500.0</td>\n",
       "      <td>343600.0</td>\n",
       "      <td>194000.0</td>\n",
       "      <td>111300.0</td>\n",
       "      <td>82300.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>06</td>\n",
       "      <td>Jun</td>\n",
       "      <td>'11</td>\n",
       "      <td>Jun '11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-12</td>\n",
       "      <td>438900.0</td>\n",
       "      <td>333200.0</td>\n",
       "      <td>194600.0</td>\n",
       "      <td>103800.0</td>\n",
       "      <td>98800.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>'11</td>\n",
       "      <td>Dec '11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-06</td>\n",
       "      <td>470600.0</td>\n",
       "      <td>341700.0</td>\n",
       "      <td>196000.0</td>\n",
       "      <td>108200.0</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>06</td>\n",
       "      <td>Jun</td>\n",
       "      <td>'12</td>\n",
       "      <td>Jun '12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-12</td>\n",
       "      <td>492300.0</td>\n",
       "      <td>400400.0</td>\n",
       "      <td>203800.0</td>\n",
       "      <td>111500.0</td>\n",
       "      <td>142800.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>'12</td>\n",
       "      <td>Dec '12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearMonth  New York  Los Angeles   Chicago  Philadelphia   Phoenix  Year  \\\n",
       "0   2010-12  449900.0     364500.0  215200.0      105600.0   84600.0  2010   \n",
       "1   2011-06  463500.0     343600.0  194000.0      111300.0   82300.0  2011   \n",
       "2   2011-12  438900.0     333200.0  194600.0      103800.0   98800.0  2011   \n",
       "3   2012-06  470600.0     341700.0  196000.0      108200.0  122000.0  2012   \n",
       "4   2012-12  492300.0     400400.0  203800.0      111500.0  142800.0  2012   \n",
       "\n",
       "  Month MonthName   Yr  MonthYr  \n",
       "0    12       Dec  '10  Dec '10  \n",
       "1    06       Jun  '11  Jun '11  \n",
       "2    12       Dec  '11  Dec '11  \n",
       "3    06       Jun  '12  Jun '12  \n",
       "4    12       Dec  '12  Dec '12  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cities_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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vxbZoNEAvv/SyfstrEOS49w+xT/r7bdejHunx2OCFAa6P8LzzPC8CPkO/AWU80t1t/S6S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\nqrmEH6y15yhgjeqaTa0wrtXt1jTUclrTW6S8KaiTcmcYBu1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XWLx4sU/379evH3feeSdt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airJsL692DzVMHhV+HYRph6E9zxS3D7l4JWnIWctxuTcxVFbV8/OgEyFjcuaU0c092vCICc4nJ+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d043b69438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=df_cities_T.plot(kind='line', x='MonthYr', figsize=(20,12), grid=True, style='o-')\n",
    "ax.yaxis.set_major_formatter(tick.FuncFormatter(fmt_x))\n",
    "plt.xlabel(\"Mon 'Yr\", labelpad=20)\n",
    "plt.ylabel(\"Median Sale Price (in $)\", labelpad=20)\n",
    "plt.title(\"Median Price growth in Top 10 Metros by Size (2010 - 2017)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Analysis 3 : Among the Top 5 metros by size, Los Angeles saw the highest percentage increase in median sale price(150%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Median Sale Price increase for Cities in Bay area (San Jose, San Francisco & Santa Clara)<a class=\"anchor\" id=\"plot4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>StateName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2008-03</th>\n",
       "      <th>2008-04</th>\n",
       "      <th>2008-05</th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-07</th>\n",
       "      <th>2008-08</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12447.0</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>2</td>\n",
       "      <td>508700.0</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>459500.0</td>\n",
       "      <td>449200.0</td>\n",
       "      <td>432600.0</td>\n",
       "      <td>419400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>647500.0</td>\n",
       "      <td>663500.0</td>\n",
       "      <td>672200.0</td>\n",
       "      <td>687000.0</td>\n",
       "      <td>696900.0</td>\n",
       "      <td>704600.0</td>\n",
       "      <td>695900.0</td>\n",
       "      <td>685900.0</td>\n",
       "      <td>688000.0</td>\n",
       "      <td>684800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17426.0</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>3</td>\n",
       "      <td>324300.0</td>\n",
       "      <td>313100.0</td>\n",
       "      <td>291700.0</td>\n",
       "      <td>282100.0</td>\n",
       "      <td>277400.0</td>\n",
       "      <td>273700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>267900.0</td>\n",
       "      <td>271400.0</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>280700.0</td>\n",
       "      <td>286400.0</td>\n",
       "      <td>299400.0</td>\n",
       "      <td>298500.0</td>\n",
       "      <td>293100.0</td>\n",
       "      <td>277800.0</td>\n",
       "      <td>272500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40326.0</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5</td>\n",
       "      <td>220900.0</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>206600.0</td>\n",
       "      <td>198300.0</td>\n",
       "      <td>187700.0</td>\n",
       "      <td>177100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>223100.0</td>\n",
       "      <td>226000.0</td>\n",
       "      <td>230100.0</td>\n",
       "      <td>236800.0</td>\n",
       "      <td>239400.0</td>\n",
       "      <td>238200.0</td>\n",
       "      <td>235800.0</td>\n",
       "      <td>233700.0</td>\n",
       "      <td>237000.0</td>\n",
       "      <td>237900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54296.0</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>California</td>\n",
       "      <td>8</td>\n",
       "      <td>419800.0</td>\n",
       "      <td>408200.0</td>\n",
       "      <td>398800.0</td>\n",
       "      <td>380600.0</td>\n",
       "      <td>371300.0</td>\n",
       "      <td>356600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>553600.0</td>\n",
       "      <td>566300.0</td>\n",
       "      <td>574300.0</td>\n",
       "      <td>577800.0</td>\n",
       "      <td>572000.0</td>\n",
       "      <td>573200.0</td>\n",
       "      <td>577600.0</td>\n",
       "      <td>583200.0</td>\n",
       "      <td>584200.0</td>\n",
       "      <td>585000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33839.0</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>California</td>\n",
       "      <td>10</td>\n",
       "      <td>615200.0</td>\n",
       "      <td>578200.0</td>\n",
       "      <td>560700.0</td>\n",
       "      <td>543300.0</td>\n",
       "      <td>529300.0</td>\n",
       "      <td>517700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>885300.0</td>\n",
       "      <td>922800.0</td>\n",
       "      <td>970900.0</td>\n",
       "      <td>1002300.0</td>\n",
       "      <td>1044300.0</td>\n",
       "      <td>1060800.0</td>\n",
       "      <td>1071700.0</td>\n",
       "      <td>1065200.0</td>\n",
       "      <td>1054600.0</td>\n",
       "      <td>1030600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID   RegionName   StateName  SizeRank   2008-03   2008-04   2008-05  \\\n",
       "1   12447.0  Los Angeles  California         2  508700.0  480000.0  459500.0   \n",
       "2   17426.0      Chicago    Illinois         3  324300.0  313100.0  291700.0   \n",
       "4   40326.0      Phoenix     Arizona         5  220900.0  213000.0  206600.0   \n",
       "7   54296.0    San Diego  California         8  419800.0  408200.0  398800.0   \n",
       "9   33839.0     San Jose  California        10  615200.0  578200.0  560700.0   \n",
       "\n",
       "    2008-06   2008-07   2008-08    ...       2017-10   2017-11   2017-12  \\\n",
       "1  449200.0  432600.0  419400.0    ...      647500.0  663500.0  672200.0   \n",
       "2  282100.0  277400.0  273700.0    ...      267900.0  271400.0  275000.0   \n",
       "4  198300.0  187700.0  177100.0    ...      223100.0  226000.0  230100.0   \n",
       "7  380600.0  371300.0  356600.0    ...      553600.0  566300.0  574300.0   \n",
       "9  543300.0  529300.0  517700.0    ...      885300.0  922800.0  970900.0   \n",
       "\n",
       "     2018-01    2018-02    2018-03    2018-04    2018-05    2018-06    2018-07  \n",
       "1   687000.0   696900.0   704600.0   695900.0   685900.0   688000.0   684800.0  \n",
       "2   280700.0   286400.0   299400.0   298500.0   293100.0   277800.0   272500.0  \n",
       "4   236800.0   239400.0   238200.0   235800.0   233700.0   237000.0   237900.0  \n",
       "7   577800.0   572000.0   573200.0   577600.0   583200.0   584200.0   585000.0  \n",
       "9  1002300.0  1044300.0  1060800.0  1071700.0  1065200.0  1054600.0  1030600.0  \n",
       "\n",
       "[5 rows x 129 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SalePrice_City.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_SalePrice_City1 = pd.read_csv(\"Median Price\\Sale_Prices_City.CSV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>StateName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2008-03</th>\n",
       "      <th>2008-04</th>\n",
       "      <th>2008-05</th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-07</th>\n",
       "      <th>2008-08</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33839.0</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>California</td>\n",
       "      <td>10</td>\n",
       "      <td>615200.0</td>\n",
       "      <td>578200.0</td>\n",
       "      <td>560700.0</td>\n",
       "      <td>543300.0</td>\n",
       "      <td>529300.0</td>\n",
       "      <td>517700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>885300.0</td>\n",
       "      <td>922800.0</td>\n",
       "      <td>970900.0</td>\n",
       "      <td>1002300.0</td>\n",
       "      <td>1044300.0</td>\n",
       "      <td>1060800.0</td>\n",
       "      <td>1071700.0</td>\n",
       "      <td>1065200.0</td>\n",
       "      <td>1054600.0</td>\n",
       "      <td>1030600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20330.0</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>12</td>\n",
       "      <td>792500.0</td>\n",
       "      <td>784400.0</td>\n",
       "      <td>782400.0</td>\n",
       "      <td>779000.0</td>\n",
       "      <td>787300.0</td>\n",
       "      <td>793600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1282800.0</td>\n",
       "      <td>1290600.0</td>\n",
       "      <td>1287900.0</td>\n",
       "      <td>1291100.0</td>\n",
       "      <td>1288100.0</td>\n",
       "      <td>1307000.0</td>\n",
       "      <td>1318200.0</td>\n",
       "      <td>1327100.0</td>\n",
       "      <td>1316400.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>13713.0</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>California</td>\n",
       "      <td>171</td>\n",
       "      <td>586300.0</td>\n",
       "      <td>581300.0</td>\n",
       "      <td>575200.0</td>\n",
       "      <td>600700.0</td>\n",
       "      <td>611200.0</td>\n",
       "      <td>620700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1166700.0</td>\n",
       "      <td>1211000.0</td>\n",
       "      <td>1255200.0</td>\n",
       "      <td>1300200.0</td>\n",
       "      <td>1352900.0</td>\n",
       "      <td>1355300.0</td>\n",
       "      <td>1382300.0</td>\n",
       "      <td>1324100.0</td>\n",
       "      <td>1304400.0</td>\n",
       "      <td>1307000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     RegionID     RegionName   StateName  SizeRank   2008-03   2008-04  \\\n",
       "9     33839.0       San Jose  California        10  615200.0  578200.0   \n",
       "11    20330.0  San Francisco  California        12  792500.0  784400.0   \n",
       "170   13713.0    Santa Clara  California       171  586300.0  581300.0   \n",
       "\n",
       "      2008-05   2008-06   2008-07   2008-08    ...        2017-10    2017-11  \\\n",
       "9    560700.0  543300.0  529300.0  517700.0    ...       885300.0   922800.0   \n",
       "11   782400.0  779000.0  787300.0  793600.0    ...      1282800.0  1290600.0   \n",
       "170  575200.0  600700.0  611200.0  620700.0    ...      1166700.0  1211000.0   \n",
       "\n",
       "       2017-12    2018-01    2018-02    2018-03    2018-04    2018-05  \\\n",
       "9     970900.0  1002300.0  1044300.0  1060800.0  1071700.0  1065200.0   \n",
       "11   1287900.0  1291100.0  1288100.0  1307000.0  1318200.0  1327100.0   \n",
       "170  1255200.0  1300200.0  1352900.0  1355300.0  1382300.0  1324100.0   \n",
       "\n",
       "       2018-06    2018-07  \n",
       "9    1054600.0  1030600.0  \n",
       "11   1316400.0        NaN  \n",
       "170  1304400.0  1307000.0  \n",
       "\n",
       "[3 rows x 129 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_BayArea = df_SalePrice_City1[(df_SalePrice_City['RegionName']=='Santa Clara') | (df_SalePrice_City1['RegionName']=='San Jose') | (df_SalePrice_City1['RegionName']=='San Francisco')]\n",
    "df_BayArea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-12</th>\n",
       "      <th>2009-06</th>\n",
       "      <th>2009-12</th>\n",
       "      <th>2010-06</th>\n",
       "      <th>2010-12</th>\n",
       "      <th>2011-06</th>\n",
       "      <th>2011-12</th>\n",
       "      <th>2012-06</th>\n",
       "      <th>...</th>\n",
       "      <th>2013-12</th>\n",
       "      <th>2014-06</th>\n",
       "      <th>2014-12</th>\n",
       "      <th>2015-06</th>\n",
       "      <th>2015-12</th>\n",
       "      <th>2016-06</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-06</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>543300.0</td>\n",
       "      <td>428300.0</td>\n",
       "      <td>366700.0</td>\n",
       "      <td>448600.0</td>\n",
       "      <td>446400.0</td>\n",
       "      <td>448600.0</td>\n",
       "      <td>422500.0</td>\n",
       "      <td>422600.0</td>\n",
       "      <td>450200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>616700.0</td>\n",
       "      <td>640200.0</td>\n",
       "      <td>669100.0</td>\n",
       "      <td>705300.0</td>\n",
       "      <td>738400.0</td>\n",
       "      <td>760300.0</td>\n",
       "      <td>785600.0</td>\n",
       "      <td>817400.0</td>\n",
       "      <td>970900.0</td>\n",
       "      <td>1054600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>779000.0</td>\n",
       "      <td>704300.0</td>\n",
       "      <td>666400.0</td>\n",
       "      <td>713700.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>686300.0</td>\n",
       "      <td>660700.0</td>\n",
       "      <td>654700.0</td>\n",
       "      <td>695700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>869400.0</td>\n",
       "      <td>929000.0</td>\n",
       "      <td>1026100.0</td>\n",
       "      <td>1135600.0</td>\n",
       "      <td>1150800.0</td>\n",
       "      <td>1157100.0</td>\n",
       "      <td>1206500.0</td>\n",
       "      <td>1203100.0</td>\n",
       "      <td>1287900.0</td>\n",
       "      <td>1316400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>600700.0</td>\n",
       "      <td>539800.0</td>\n",
       "      <td>495800.0</td>\n",
       "      <td>579900.0</td>\n",
       "      <td>519200.0</td>\n",
       "      <td>549700.0</td>\n",
       "      <td>508300.0</td>\n",
       "      <td>536100.0</td>\n",
       "      <td>564600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>700500.0</td>\n",
       "      <td>696900.0</td>\n",
       "      <td>753500.0</td>\n",
       "      <td>845700.0</td>\n",
       "      <td>904800.0</td>\n",
       "      <td>926500.0</td>\n",
       "      <td>968800.0</td>\n",
       "      <td>1042900.0</td>\n",
       "      <td>1255200.0</td>\n",
       "      <td>1304400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        RegionName   2008-06   2008-12   2009-06   2009-12   2010-06  \\\n",
       "9         San Jose  543300.0  428300.0  366700.0  448600.0  446400.0   \n",
       "11   San Francisco  779000.0  704300.0  666400.0  713700.0  670000.0   \n",
       "170    Santa Clara  600700.0  539800.0  495800.0  579900.0  519200.0   \n",
       "\n",
       "      2010-12   2011-06   2011-12   2012-06    ...       2013-12   2014-06  \\\n",
       "9    448600.0  422500.0  422600.0  450200.0    ...      616700.0  640200.0   \n",
       "11   686300.0  660700.0  654700.0  695700.0    ...      869400.0  929000.0   \n",
       "170  549700.0  508300.0  536100.0  564600.0    ...      700500.0  696900.0   \n",
       "\n",
       "       2014-12    2015-06    2015-12    2016-06    2016-12    2017-06  \\\n",
       "9     669100.0   705300.0   738400.0   760300.0   785600.0   817400.0   \n",
       "11   1026100.0  1135600.0  1150800.0  1157100.0  1206500.0  1203100.0   \n",
       "170   753500.0   845700.0   904800.0   926500.0   968800.0  1042900.0   \n",
       "\n",
       "       2017-12    2018-06  \n",
       "9     970900.0  1054600.0  \n",
       "11   1287900.0  1316400.0  \n",
       "170  1255200.0  1304400.0  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Subset columns\n",
    "df_BayArea = df_BayArea.iloc[0:,1::6]\n",
    "df_BayArea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>RegionName</th>\n",
       "      <th>San Jose</th>\n",
       "      <th>San Francisco</th>\n",
       "      <th>Santa Clara</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-06</th>\n",
       "      <td>543300.0</td>\n",
       "      <td>779000.0</td>\n",
       "      <td>600700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12</th>\n",
       "      <td>428300.0</td>\n",
       "      <td>704300.0</td>\n",
       "      <td>539800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06</th>\n",
       "      <td>366700.0</td>\n",
       "      <td>666400.0</td>\n",
       "      <td>495800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12</th>\n",
       "      <td>448600.0</td>\n",
       "      <td>713700.0</td>\n",
       "      <td>579900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06</th>\n",
       "      <td>446400.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>519200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RegionName  San Jose  San Francisco  Santa Clara\n",
       "2008-06     543300.0       779000.0     600700.0\n",
       "2008-12     428300.0       704300.0     539800.0\n",
       "2009-06     366700.0       666400.0     495800.0\n",
       "2009-12     448600.0       713700.0     579900.0\n",
       "2010-06     446400.0       670000.0     519200.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_BayArea_T = df_BayArea.set_index('RegionName').T\n",
    "df_BayArea_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##Reset index\n",
    "df_BayArea_T.reset_index(level=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##Name columns\n",
    "df_BayArea_T.columns = ['YearMonth','San Jose', 'San Francisco', 'Santa Clara']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_BayArea_T['Year']= (df_BayArea_T['YearMonth'].str.split('-').str[0])\n",
    "df_BayArea_T['Month']= (df_BayArea_T['YearMonth'].str.split('-').str[1])\n",
    "\n",
    "df_BayArea_T['MonthName']=df_BayArea_T['Month'].apply(lambda x: 'Jun' if x == '06' else 'Dec')\n",
    "df_BayArea_T['Yr']=df_BayArea_T['Year'].apply(lambda x: '\\''+x[-2:])\n",
    "df_BayArea_T['MonthYr']=df_BayArea_T['MonthName'] +' '+ df_BayArea_T['Yr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>San Jose</th>\n",
       "      <th>San Francisco</th>\n",
       "      <th>Santa Clara</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>Yr</th>\n",
       "      <th>MonthYr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-06</td>\n",
       "      <td>543300.0</td>\n",
       "      <td>779000.0</td>\n",
       "      <td>600700.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>06</td>\n",
       "      <td>Jun</td>\n",
       "      <td>'08</td>\n",
       "      <td>Jun '08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>428300.0</td>\n",
       "      <td>704300.0</td>\n",
       "      <td>539800.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>'08</td>\n",
       "      <td>Dec '08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-06</td>\n",
       "      <td>366700.0</td>\n",
       "      <td>666400.0</td>\n",
       "      <td>495800.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>06</td>\n",
       "      <td>Jun</td>\n",
       "      <td>'09</td>\n",
       "      <td>Jun '09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12</td>\n",
       "      <td>448600.0</td>\n",
       "      <td>713700.0</td>\n",
       "      <td>579900.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>Dec</td>\n",
       "      <td>'09</td>\n",
       "      <td>Dec '09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-06</td>\n",
       "      <td>446400.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>519200.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>06</td>\n",
       "      <td>Jun</td>\n",
       "      <td>'10</td>\n",
       "      <td>Jun '10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearMonth  San Jose  San Francisco  Santa Clara  Year Month MonthName   Yr  \\\n",
       "0   2008-06  543300.0       779000.0     600700.0  2008    06       Jun  '08   \n",
       "1   2008-12  428300.0       704300.0     539800.0  2008    12       Dec  '08   \n",
       "2   2009-06  366700.0       666400.0     495800.0  2009    06       Jun  '09   \n",
       "3   2009-12  448600.0       713700.0     579900.0  2009    12       Dec  '09   \n",
       "4   2010-06  446400.0       670000.0     519200.0  2010    06       Jun  '10   \n",
       "\n",
       "   MonthYr  \n",
       "0  Jun '08  \n",
       "1  Dec '08  \n",
       "2  Jun '09  \n",
       "3  Dec '09  \n",
       "4  Jun '10  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_BayArea_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YqbWRvq6G4/kV6uW3ligJY6eRec7HOwel8VlmxUNdQl9nF0VI0z3OYx7uzHW/\nXSXMAXcUvOtaFWvtkxCM9f/mPbidEyVptLhY8KxeovkJ6haW1o9/YZGg6cAlxfvxFKAHzaLjMGYJ\noWlzgdfO+aH0FmUIunZQ1L9jpLhIeePFP3auJ+TjriS8PnZ5KXzhJbJNefVwThTlv9dXNpvgaaeF\nGGO2GWOeAf7hgqJdi0iyxNJvF0WxUipPGvn++/27eYrKQEtOd38s6X72MXa6YSbWas7zJzi2lFMb\nPQXfVOzU1UjbyS7d70WkSSnK8dryhRJanCxWvHqLdL8+rxTyHEz81sD+Kb9enfSm9JwdpU96/WqN\nsW4rPBI2NohIa+BT7AqtWcAVpgQXAM6iyvN9GmmxlK6EVikP9xfq7XcTkdSwOFyYNwaE+zD0zj1a\nG/Yr66K56Agnlnr1xq9jRaRFhDRnYJWr/vRlxbMuzYySpn2CyoobEbkOeAlbd48ZY/5RQhaw7c1g\n20mke4j3bLq8pPtSnMTTjnLdBwBFOSxRhaCiKIUYu/rmMOAp4C4TvOJoEGOxN8+GwF+iJRSRcF86\n77nfWyI4xb6G+L/g7ibkhPjk8EjnX/DOOI95uPMedqW2WsD/xJLBWbp5isTHor3kiV3AJZ4pMt7q\nf7eISLGv1iJyBXBCHMcLwltBuIezcAgvozUh/1/vBuSfjbVgbUJohc4sL9JYJ9grgLaELFIjKQQ9\nS5Fwp/NKGG7cmex2743gaP/P2LrMJbT6YTiXiEhQ/28LXOR23wuPLyUzsf7kngLuiUNx85r7/R0l\njEkBY2ci25TXV5oAfwwouyahFdDf9YWnOSu2SHi+veIZGzxZOopI1wBZmhJScgX123LFZ7UH1sfd\nOl+058uwWLtzDKEEC0FneeW1i//BvggbX1g8slYgtOjTe8aYnVG2JVjlhGB9DsbLm9j+WJMSVrIP\naMsR683dV4aVQp4yIyLVROTMklNyrfvdQVE/vWPc7/kiEnVaYUCdeNQFbglIX5XQmBE+jnljQ5Wy\nWPeLXTRnhpNhDtDLxL4ombd68s0Rzu0e9/vfcItQbL/Oxyrer6U412GfN/MoPgZ4K8v/zvk8DMKr\nzxwiL6IRRCz1Ohs7I0OI/Dzsraj7bQJnFHznfltJwGJHInIJCVohOF5E5HLse0IKMNIY82As+Zxi\n2fvwem/AcWsT+mAS92JLJeC1o95BLi/cuOSNk18nuGxFObgYY3TTTbejbMM+pBrgnTjzrXf5+gXE\nPeXiDPBJOGeJAAAgAElEQVQccLwvrir2K/G/gelh+WphpxoZ7Mprp7jwdOyL6V7sl08DjAko1yuz\naVj4HBf+LdDWhaVgLQ1WYl/kDG5xx7C8WZHO05fmZ5ema5x1mBXpXErINyyWfNHSEXLibIBngSa+\nemkC3A38T1ie9lhn0p6fqe5Ami9fa+D/YX29NI3jfOoB291x5wCtXXga9gV2N9ZKtdi5YBXEgdcu\nLF0F7EOdAdY42cXFnefCDNYirWKEYyzw1dleID0sfrQv3gBtIhznny7+c6BGhDT9XJqssraDONtF\nYB+Kt75jkKGpr6yo/QY4BbuyqMH6VGvmwithlYF5Lu6JgLyLXdxOd427uXDBjkPrXPxcICXOc5jo\n8o6KM99el++ygLhXXFwB1ol5Y19cdeyKmO9i/RL68zVweQxwXpSyvfGzbQkyvu3S7cG+6KS78DZY\nH2kG2yczw67pSuA+7BSrFBeeivUpuMXlezGOukrBriBtsC/Wl/iOe7Yrz2CnclUt7flGKLutr422\nDYsT7Iqyb/nSDAtLc4Yv7hGgmguvhe2D+YTuPSNK6CsFvmP9t5R9rrfLn4v1o1tS+qEu/YpStqG/\n+WR+DfidL64y1urzZeCrsHzPuzxbgAt91/sU7D1zD6HxIO7rWtoNa21lsB96bgFO8MVVxK4iPNl3\nzn8PaMvTXNwB4EGgni++Lla5/Anwr7C8IwiNY9mu/AouriWhle53AscGlOt9GO1fynNvQugeOY8I\n960o+WsQerb73Ks7rMLYu955wFkR8nv31l1Yv5bititdmAGeC8hX33fum4Crgcou7ljgad/1ej7O\nc4qpXrF+MP3jQE0X3gDrB9SLuyTO8iOOTy5+uYv7Gtf3XDu9wcm9g4Cxx3fcnVHKvsylWRynzBcQ\n6rsv4p7B4sh/DnbcNK7deHXZ3LUrg/UdWC1C/tqun9XFLgDi1d+FvvC6QGpYvh6+tPOBjtjn0xTs\nPXGaL/6q0vQx3XQ7VLakC6Cbbrod/I3yUQim+h7yvM1T6vhfbGYG5O0C7POl8R6ADVZZ9CjxKwTP\nDDvmXt/+dqxFl+HoUggK1pm2/xrt9D2sRcrXg9ALofdisw37dd1/rOPiPKcLfdc5/LrP9l330WH5\nmka6dgFlNPddK4Od2vWbb38NvpfWgPzDfWk/DYi/zhe/jQgPu9gXOK+ec7FTAn8GZvvS9EMVgv70\n17q68vL8Grb/IQGKXEIKwTsIjVl7w677OpySMc5zKA+FYEXsxxJ/X9rl+oN/7PwgIO+HYfXzs9su\n8KWJVZlTg5Aizuvn/n6/hzDFY9g19Y8Neb6wJUDdOOsrk9DLrcGO3Xt8+5uAUyPkTZRCcJsrx9v8\nY5XBWmWlBRzD/8JfQMhXrpfHU0hEVAi64/hfOP9Yyj7ntY9pMaZv6SuzYynakGAXDPDX096wOjDY\nqd7+fMcS6qvG1bWneMnBKoRKfV1Luzm58sPO5wD2GaIgLPz1CO2hJnbKrb9N/Oo7P2+LpBB8hqJK\nxV/DZLk4gux+xdduQmNDTApCit77fg3rC+Hb3yMcoxMh5Z1x19Crz3xgUJTyK2EVsf4xYL9vfyaQ\nESFvT0Jjrlfn/vHDy1+5FG0ipnp1181Ll0fId7IX9mApyvaPT8XGP+yHzpww+bz9iGMP5asQXOiT\nZ3MJ7eiiCMe4M6zd+O9LO4Ezo5TvTxttC1KwPhx2zXLC2qABHj8YY5FuupXnplOGFUVJCMaYfGPM\nIOzXvDewipYK2Ie6tdgpojdiHyrC884CTgPGYR0sV8Q+YA3D+omJdeqy/5hfYb/oTcQ+zKZjrQ9G\nYx9+vomc+8jEWIZgfViNwyqlKmEf2Bdiv2T/MyDfx9gpjf9w6bKxLzm7sQrb/wFaGWPWxCnPf7AW\niOOxL1gVsVY/D2EfbD1fh/GuvOgvYxV2pc+/YxUTHkuw09pOMcasjHKIWRH+B4V9bowxEeRYjrVO\n+wRb38dg/c9E8/dzVGOMeRt77cZiFXiVsS95WViLh8tMdLcGG4B2wAvYl7E0d5xngdOMMT+Vm/Bx\nYIw5YIzpi20f72BlzHDbz9gXuWsJnsbZF6vkX4XtL8e5rXIp5NiF9ev1J6z1ZLaTYTX2Y09rY0y4\nv64N2DH9Waw17Q6sYnEv1n/avcAZJrbV6v2yrMdeu6FYBW8B9qPTcuBxrCVueY/hdSi6Mm0K1hL6\nQ6C3MaaPKb5gCMAArBXrd9gXyBSsxc6t2NWCA8eIACa4313YVV3jQkTqYT/m+I8VFTdOef5db4y3\nTHePeQBrKfkatu2kYFf13Yj1SzyAMB++xk4Z7YC1lv3F5dmLtYztaIyJ+/wTgZMrE3vt3sL6PD6A\nbeO/YevqNeD3xpgbgtqDsSsHX4i1bPsIq/iogm3PK7GKxEux1oNB5GOthP+K7ecZ2PvlBGzfmhoh\n333Ye+n32Ocfb2yoHuPp+98RaxJ5xeYGkY5pjJmDnQY+GvtMWAmraP8A6GyMeT5S4cZOTT4P66pg\nAbYe8rDWWn8GupsIftuMMVOwVlz/wvbDfdh624pVrt7k8u8roQ6CiKlejTF3Yq2bp2KfQatin0HH\nA+cYY+JZaMnD7z6j2NRtNz53Bf6DHTfSsG30T8Q39iQSfzuqT/R2FLgwh7H+aLtgn+e3YdvRGmAU\n9hnuq6B8ZcUY8xBwLvad5ifsfUiw9+h3sbMP7i+PshXlYOJNnVIURVGUQwoR+RyrYL7JGDMmyeIo\nhwkishirSLzcGBPJv6CiHNKIyHtYJdJoY8xtyZZHObiIyAis4utpY8xdJaVXjnxEpBdWqQxQxxRd\nSEZRFKVUqIWgoiiKcsghIh2xysACiq8iqCiKcsTiFr76g9t9KZmyKIpyyHCu+12vykBFURJFqVee\nUhRFUZSyICIDsc6cxwE/G2Py3cqJvbFTfQDeNcasi3QMRVGUIwkRycD6+qqAdUOwIMkiKYqSREQk\nHeuaYZALOuirqyuKcuSiCkFFURQlWTTB+gj7J5AvIruwvoo86/XFWGfSiqIoRzQi0h/rj7U+1kdW\nHtYHo6IoRykiMh3r9zndBa0mwNezoihKaVGFoKIoipIs3sG++HbBOm6vjV2oZCnW8fYo51hcURTl\nSKc6dnGC/cA84P8ZY75OrkiKoiSZuljXKauAycAjOl1YUZREoouKHCWIyCKgGXbVtlVJFkdRFEVR\nFEVRFEVRFOVIoDl2RfGfjDGnJVuYWFGF4FGCiOwEaiRbDkVRFEVRFEVRFEVRlCOQXcaYmskWIlZ0\nyvDRw16gRo0aNWjbtm2yZVEOcbZt2wZA3bp1kyyJcqijbUWJB20vSqxoW1HiQduLEivaVpR40Pai\nxMq8efPYt28fWL3LYYMqBI8eVgGN2rZtS1ZWVrJlUQ5xPvzwQwAuvfTSJEuiHOpoW1HiQduLEiva\nVpR40PaixIq2FSUetL0osdKmTRu+//57OMzcs6WUnERRFEVRFEVRFEVRFEVRlCMFVQgqiqIoiqIo\niqIoiqIoylGEKgQVRVEURVEURVEURVEU5ShCFYKKoiiKoiiKoiiKoiiKchShCkFFURRFURRFURRF\nURRFOYpQhaCiKIqiKIqiKIqiKIqiHEWoQlBRFEVRFEVRFEVRFEVRjiLSki2Acuiyd+9edu/ezb59\n+8jLy8MYk2yRlINE/fr1AVi2bFmSJVEARIS0tDQqV65M9erVqVq1arJFUhRFURRFURRFUQ5jVCGo\nFMMYw5YtW9ixY0eyRVGSRLVq1ZItguLDGENubi67du1i165d1K5dm/r16yMiyRZNURRFURRFURRF\nOQxRhaBSjN27d7Njxw5EhDp16lCtWjUqVKhASorOMD9a2LlzJwA1a9ZMsiQKQEFBATk5OezZs4ft\n27ezY8cOMjIyqFGjRrJFUxRFURRFURRFUQ5DVCGoFOPXX38FoEGDBtSqVSvJ0iiKkpKSQkZGBhkZ\nGaSlpbFp0yZ+/fVXVQgqiqIoiqIoiqIopUJNvpRiZGdnA1C9evUkS6IoSjhev/T6qaIoiqIoiqIo\niqLEiyoElWJ4i4ekpqYmWRJFUcLxpu7rIj+KoiiKoiiKoihKaVGFoKIoymGELiSiKIqiKIqiKIqi\nlBVVCCqKoiiKoiiKoiiKoijKUYQqBBVFURRFURRFURRFURTlKEIVgoqiKIqiKIqiKIqiKIpyFKEK\nQUUpA8uXL+e2226jRYsWVK5cmUqVKtGkSRM6derEPffcw7Rp05ItYkSysrIQEZo2bZpsURRFURRF\nURRFURRFOYikJVsARTlcGTduHDfccAM5OTk0atSIrl27UqtWLbZu3crChQv58ssvmTVrFueff36y\nRVUURVEURVEURVEURSlEFYJK0jHGsHjdTl76fDUzl28lOzefjPRUurWszy2dj+fUzBqH3MqqmzZt\non///uTk5DB8+HAGDx5MampqYXxBQQGzZ89m9uzZSZRSURRFURRFURRFURSlOKoQVJJKbn4Bd7+7\nmOlLt3AgL58CY8P35+bz8ZJf+O/yLXQ/qT7Dr2pLeuqhM8N98uTJ7Nu3j44dOzJkyJBi8SkpKXTu\n3JnOnTsnQTpFURRFURRFURRFUZTIHDoaFuWowxjD3e8uZtrSzezPDSkDPQqMVQxOW7qZu99djDEm\n+EBJYMuWLQDUr18/7rxfffUV9913H+3bt6dBgwZUqFCBhg0bcuWVVzJ37tzAPMOGDUNEGDZsGJs3\nb+bWW28lMzOTihUr0qxZMx588EGys7PLdE7hrF27lkGDBnH88cdTsWJFatWqxe9//3veeuutwPT5\n+fmMGjWKTp06UaNGDSpUqECDBg04/fTTueeee9i6dWuxPL/99huPP/44Z5xxBtWrV6dSpUq0bt2a\nYcOGsXfv3oSej6IoiqIoiqIoiqIkBGNg/Xx49wbq7F2RbGlKhVoIKklj8bqdTF+6hezcgqjpsnML\nmL50C9+s30XbxjUPknTRadKkCQAzZsxgyZIltGnTJua8Q4cOJSsri9atW9OhQwcqVqzIihUreP/9\n95k4cSJvv/02ffr0Ccy7bt062rVrhzGGTp06sXv3bmbPns1jjz3G0qVL+eijjxJyfvPnz+fKK69k\n165dNGvWjMsvv5zt27cza9YssrKy+OSTTxg7dmyRqdw333wzY8eOpVKlSpxzzjnUrVuXbdu28eOP\nPzJ8+HD69OlDvXr1CtOvX7+eCy+8kKVLl1KvXj06duxIRkYG8+bN4+GHH+aDDz4gKyuLWrVqJeSc\nFEVRFEVRFEVRFKXM5OfCB7fBiqmQl41w6BgvxYMqBJWk8fLnP3EgLz+mtAfy8nn589U8e93p5SxV\nbFx66aU0bNiQjRs3ctppp3HBBRfQpUsX2rVrR/v27alRo0bEvPfeey9vvvkmDRo0KBI+adIkrrji\nCm677TZ69uxJ5cqVi+V99dVXGTBgAM899xwVKlQAYNmyZXTo0IFJkybxxRdfcPbZZ5fp3LKzs+nX\nrx+7du3irrvu4sknnyz0j7hkyRLOO+88/v3vf3P22Wdz6623ArBmzRrGjh1L48aNmTdvXrFzW7x4\nMQ0bNizcN8Zw1VVXsXTpUv70pz/x2GOPFZ7v/v37GThwIG+88QZDhgxhzJgxZTofRVEURVEURVEU\nRUkIxjhl4BTI3Z9sacqEKgSVUtH0wSkHtbwCA5O//YXJ35a93J//r2eZj1GtWjWmT5/ODTfcwPz5\n85k6dSpTp04FrP/As846i8GDB3P11VcXy3vRRRcFHrNXr1706dOHt956i5kzZ9KzZ3E5GzduzMiR\nIwuVgQCtWrWib9++vPDCC8yYMaPMCsH33nuPDRs20LhxYx5//PEii6W0adOGYcOGMWjQIJ588slC\nhaA3hfr0008vpgwEaNu2bZH9Tz75hC+//JKzzjqLp59+mpSUkPeCSpUqMWrUKD799FPefPNN/vWv\nf6mVoKIoiqIoiqIoipJ8NiywloGHuTIQ1IegopSaVq1aMW/ePL744gv++te/ct5551GrVi0KCgqY\nM2cO11xzDf369QvMu23bNsaMGcO9997LgAED6NevH/369WPJkiUArFy5MjBft27dqFSpUrHwli1b\nArBx48Yyn9esWbMA6NOnD+np6cXib7rpJkSEVatWsWHDhsLyq1WrxpQpU3jkkUdYs2ZN1DI85ekV\nV1xRRBnoUaVKFdq3b09eXh7z5s0r6ykpiqIoiqIoiqIoStmZ8wzkJdZ/f7JQC0FFKSOdOnWiU6dO\nABQUFDB37lwefvhhPv30U8aOHUvPnj2L+AQcPXo0d999N/v27Yt4zN27dweGe74Lw6levTpAQhYW\n8ZR8xx13XGB8RkYGDRs2ZMOGDWzYsIFGjRpRrVo1Xn31Vfr378/QoUMZOnQojRo1omPHjvTs2ZNr\nrrmGjIyMwmOsXr0agPvuu4/77rsvqjxBi5EoiqIoiqIoiqIoykFn5Sdgoq+DcLigCkGlVCRi2u0d\nby7k4yW/FFtdOIgUgYtPPvaQ8SEYiZSUFDp16sTUqVPp0KEDCxcuZOLEiYUKwfnz53P77beTlpbG\nE088Qa9evcjMzKRy5cqICH/961959NFHI66oHGRNl2i8sv0LhkRK4+fKK6+ke/fufPjhh3z22Wd8\n8cUXjB8/nvHjxzNs2DA+//xzGjduDNgViQG6dOlC06ZNo8oTSTGpKIqiKIqiKIqiKOXOjp9g+WRY\nNumIsQ4EVQgqSWTAuc347/It7M8teWGRimmpDDj3+IMgVWJITU2lW7duLFy4sIiF2/jx4zHGMHjw\nYO69995i+VatWnUwxQwkMzMTgJ9//jkwPjs7m19++QWARo0aFYmrWbMmN954IzfeeCMAP/74I7fc\ncgszZ87kgQce4K233gIoVAz26dOHO+64ozxOQ1EURVEURVEURVHixxjYstQqAJdNhs3fFU8CfFex\nAmOqV2dBRh4QeQbgoYr6EFSSRtvGNel+Un0y0qM3w4z0FLqfVJ9TMyOv3HuwiWTB52ft2rVASMEG\nsGPHDiCkEPOzdetWpk2bliAJS0+XLl0AeP/998nLyysWP3bsWIwxNG/evJhCMJwTTjiBoUOHAvDN\nN98Uhvfo0QOwC5goiqIoiqIoiqIoSlIpKIB18+DTv8Ezp8MLnSDr0UBlYC7wQL063HxMfWZUqcTh\nOoFYFYJK0hARhl/VlvNPakCl9FRSwmaopghUSk/l/JMaMPyqtlGnsB5snn/+eW666Sa+/vrrYnF5\neXm89NJLjB8/HqDISsPe4h+vv/46e/fuLQzfs2cP/fv3Z+fOneUsecn06dOHRo0asWbNGv7yl79Q\nUBAa3pYuXcpDDz0EUMTCcdGiRYwbN479+4uvtDRp0iSg6NTfyy67jHbt2jFr1ixuu+22QkWpn9Wr\nV/Pcc88l7LwURVEURVEURVEUpZD8XFidBVPugX+dBK90hzkjYcfqoulSK8LvLoJLn8Nc9z5DG9Rn\nZuVKZKekUHAI6SniRacMK0klPTWFkdecxjfrd/HSZ6v57/ItZOflk5GWynmt6nPLucdzauOayRaz\nGLm5uYwZM4YxY8ZwzDHH0LZtW2rXrs2OHTv49ttvC1f7vf/++7nwwgsL8910002MGDGChQsXcvzx\nx3POOedgjOGzzz6jQoUK9O/fn1dfffWgnku4X8KMjAxee+01+vTpw5NPPskHH3zAGWecwY4dO8jK\nyiInJ4e+ffsycODAwjxr1qzhmmuuoXLlypx++uk0btyYnJwcFi1axOrVq6lWrRp///vfi5Q5ceJE\nLr74YkaPHs1bb73FqaeeSmZmJtu2bWPt2rWsXLmSBg0a6JRiRVEURVEURVEUJTHk7ocfZ9rpwCum\nQnYEo5wKVeHEC6BVLzjxfKhYDYDvtnxDVuXKZB+2doEhVCGoJB0RoW3jmjx3/aG9YIifm2++maZN\nmzJ9+nS+/vprvvvuO7Zs2UJ6ejqZmZnceOONDBgwgHPOOadIvlq1ajF//nz+9re/MW3aNKZMmUL9\n+vXp3bs3f//73xk9evRBOwfPmq9KlSrF4s444ww+++wzXnjhBT755BMmTJhApUqVOOussxg4cCDX\nXXddEYvNs846i0cffZRZs2axfPlyFixYQIUKFWjcuDH33HMPd955Z7HFQTIzM/n666955ZVXePfd\nd/nuu+/46quvqFOnDo0aNeLee+/l8ssvL99KUBRFURRFURRFUY5ssnfBD9Ng2Ufww3TI/S04XeU6\n0KIHtPoDNOsC6RnFkoxd+joHylncg4UqBBWlFFSrVo3LLruMyy67LO689evXj6j4GzZsGMOGDYs5\n3KNfv37069cvLjm8BUwirfLbpEkTXnjhhZiOdcwxx/Dggw/y4IMPxiVDRkYGd9xxh1oBKoqiKIqi\nKIqiKIlj71ZrAbhskp0WXJAbnK56I2h5ibUEbNIRUqOryT5b/xkFR4B1IKhCUFGOSnbu3Fk4Nbl7\n9+5JlkZRFEVRFEVRFEVRysjOtXZV4OWTYe2XYCIo7uo0twrAVr2g4ekQhx/AA/lHin2gKgQV5aji\nrbfe4t1332Xu3Lls3ryZ5s2bM2DAgGSLpSiKoiiKoiiKoijxs3WFnQq8bDL8sjhyumNPtQrAlr2g\nXou4lIAe+QX5pKWkkRvJ2vAwQxWCinIU8fXXXzNlyhQaNmzIbbfdxrBhwwJ9CCqKoiiKoiiKoijK\nIYcxsHGRnQq8fDJsWxkhodgpwK16QcueUOu4COliKdIwe8Nshi8YfsQoA0EVgopyVDFixAhGjBiR\nbDEURVEURVEURVEUJTYK8u0U4GWTrCXg7vXB6VLS4fguVgnY4mKoWr/MRS/bvoynFjzFV798VeZj\nHWqoQlBRFEVRFEVRFEVRFEVJLMbAhgUwZyT88CnkZtuVe0+8EDoNhkZR/PflHYDVs+x04BVTYd/2\n4HTplaF5d7sy8O8ugIwaCRH9l72/8MyiZ5i8ejIGUxiekZpBZrVM1u1Zd9j7E1SFoKIoiqIoiqIo\niqIoipI48nPhg9usMi8vO7TAR+5+q+T74VNrxXf5KEhNt3EH9sKqadYScOWnkLMn+NgZNaFFD2sJ\neEI3SK+UMLH35Ozh5e9e5o2lb5BTkFMYniqp9D6xN4PaDqJGxRoM/XwoWeuzOJB3+CoFVSGoKIqi\nKIqiKIqiKIqiJAZjnDJwilUAFosvgNx9sHwKvHcT/O4i6w/wx/9CJKu7qsdYX4CtekHTc0JKxASR\nm5/LuBXjGP3taHYe2FkkrmtmV4a0G8LxNY8vDHus82Ms2baEMd+PYTWrEyrLwUIVgoqiKIqiKIqi\nKIqiKEpi2LDAWgYGKQP95O2H5ZPsFkStplYB2OoP0Kg9pKQkXFRjDJ+u+ZSnFz7Nuj3risS1rtOa\ne9rfwxnHnFEsn4hwcr2TearrU/wn7T98z/cJl628UYWgoiiKoiiKoiiKoiiKkhjmPGOnCZeGBm2g\n5SVWEdigdWQfgwlg0ZZFPDn/Sb7d+m2R8EZVGzH4tMFc1OwiUiTxSshDBVUIKoqiKIqiKIqiKIqi\nKInhh/+EfAbGgqRA94eh1SVQ+/iS05eRn3f9zIiFI5ixdkaR8OoVqjPwlIFc2/JaKqRWKHc5ko0q\nBBVFURRFURRFURRFUZTEkBuvdaCBsweXiyh+dmTv4IXFLzB+5XjyTF5heHpKOte1vI5bTrmFGhUT\ns0rx4YAqBBVFURRFURRFURRFUZTEkJ5Rsv9AP2mJWyU4iP15+3lj6Ru8suQVfsv9rUhcj2Y9GHza\nYDKrZZarDIciqhBUFEVRFEVRFEVRFEVREsPx3ewKw7EgKXaV4XIgvyCfSasn8cyiZ9iyb0uRuPYN\n2nNP+3toU7dNuZR9OKAKQUVRFEVRFEVRFEVRFKXs7N0KW5fHnj4tAzr+KeFizNkwh6cWPMXKX1cW\nCW9Woxl3t7ubLpldkHJcsORwQBWCilIGli9fzogRI5g5cybr1q3DGEO9evXIzMykY8eOXHTRRZx/\n/vnJFjOQMWPGcNNNN0VNc+qpp7J48eKDJFH5k5eXR3p6OqmpqeTl5ZWcQVEURVEURVEURYmNXRvg\n9Uthx4+xpU+rBC0uhkanJ0yEFTtWMHzBcOZsnFMkvE5GHQa1HUTvE3uTlqKqMFCFoKKUmnHjxnHD\nDTeQk5NDo0aN6Nq1K7Vq1WLr1q0sXLiQL7/8klmzZh2yCkGPBg0acNFFRU20c3JyAGjevHkyRFIU\nRVEURVEURVEOJ3b8BK//AXaudQECjdrBlqWQl1101WFJsZaBLS6Gy0dBAiz1Nv22iWcWPcOkHydh\nMIXhldIqcWPrG+nXuh9V0quUuZwjCVUIKsnHGNiwAOaMhB8+tSsSpWfAiRdCp8H2a8EhZsq7adMm\n+vfvT05ODsOHD2fw4MGkpqYWxhcUFDB79mxmz56dRCljo2XLlowZM6ZI2M6dOwGoWbNmEiQqP9LS\n0li2bNlRbxquKIqiKIqiKIqSMLYst5aBezfZ/ZR0uOJlOOlS2LAw9K6ft99aBf7uIuj0J6swLCN7\nc/byypJX+PfSf3Mg/0BheIqkcHnzyxnUdhD1K9cvczlHIqoQVJJLfi58cBusmFr0q0Huflj2kR00\nvK8GqenJldXH5MmT2bdvHx07dmTIkCHF4lNSUujcuTOdO3dOgnRKNFq2bJlsERRFURRFURRFUY4M\nNi6GN3rDvu12Py0Drn4DTnQz5TLbwVVjE15sbkEu7614j1HfjOLXA78WiTu30bkMaTeEE2udmPBy\njyRSki2AchRjjFMGToHcfUVNiMHu5+6D5VNsOmOCj5MEtmyxKxTVrx//l4avvvqK++67j/bt29Og\nQQMqVKhAw4YNufLKK5k7d25gnmHDhiEiDBs2jM2bN3PrrbeSmZlJxYoVadasGQ8++CDZ2dllOqdY\nyMzMRERYv34977//Pl26dKFWrVqICEuWLAHgp59+4pFHHqFr1640btyYihUrUrt2bbp168Y777wT\neDmodEsAACAASURBVNzp06cjInTv3p2cnBz+93//lxYtWpCRkUH9+vXp27cv69evjyjXmjVruOuu\nu2jVqhVVqlShevXqnHTSSdxxxx0sXbq0MF1eXh4iQlpa8W8h69evZ9CgQTRv3pyMjAyqVKlCkyZN\n6NGjB6+88kpguUuXLuWWW27hhBNOoFKlStSqVYtTTz2V+++/n3Xr1hVLP3v2bHr37l143Y899lj6\n9OnD119/HbXeFUVRFEVRFEVRDjnWfgVje4WUgRWqwh/fDykDywFjDNPXTOfyDy/n0a8fLaIMbFW7\nFS9f8DLPd39elYExoBaCSvLYsMBaBubuj54ub79Nt2Gh/bpwCNCkSRMAZsyYwZIlS2jTJvalyocO\nHUpWVhatW7emQ4cOVKxYkRUrVvD+++8zceJE3n77bfr06ROYd926dbRr1w5jDJ06dWL37t3Mnj2b\nxx57jKVLl/LRRx8l5PxK4rHHHuPZZ5/lzDPPpEePHqxdu5aUFPt9YezYsTz88MM0b96cVq1acfbZ\nZ7Nu3TpmzZrFzJkzmTdvHk899VTgcXNycrjwwgtZsGABnTt35qSTTuLLL7/kjTfe4PPPP+ebb76h\nRo0aRfJ8/PHHXH311ezZs4dGjRoV+kNcvXo1o0aN4thjj+Wkk06Kej4bNmygffv2bN68maZNm9Kj\nRw8qVKjAhg0bmDNnDuvWrePmm28ukue1117j1ltvJTc3lxNOOIFevXpx4MABVq1axRNPPMEpp5zC\nH//4x8L0zzzzDH/+858xxtChQwfOO+88Vq5cyfjx4/nggw948cUX6d+/f9zXQlEURVEURVEU5aDz\n40x45zprxAOQUdMqAzPbl1uRi7csZviC4SzasqhI+LFVjuXO0+6k5/E9SRG1e4sVVQgqyWPOM3aa\ncCzkZcOXz0Kf18pXphi59NJLadiwIRs3buS0007jggsuoEuXLrRr14727dsXU1r5uffee3nzzTdp\n0KBBkfBJkyZxxRVXcNttt9GzZ08qV65cLO+rr77KgAEDeO6556hQoQIAy5Yto0OHDkyaNIkvvviC\ns88+O7EnG8Do0aP5+OOPiy1GAnDxxRdz9dVX06pVqyLhK1as4LzzzmP48OFcd911tGtXXLn7+eef\n06FDB1avXk3dunUB68+wa9eufPPNN4waNYoHHnigMP1PP/3EVVddxd69e3nkkUe4//77i/hyXLNm\nDdu3b4/pfDZv3swdd9zBM888U8THYHZ2djELvrlz53LLLbcgIrz66qvFVmteunRpETkWLlzIkCFD\nSElJ4d1336V3796FcW+++SZ9+/bl9ttvp2PHjsXqTVEURVEURVEU5ZBi+VR470bIt4tRUqUe9J0I\nx8RuKBMPa3evZcTCEUxbM61IeLX0agw4ZQDXt7qeiqkVy6XsIxlVCCqlY1hkhVe5YArg+wl2KyvD\ndpX5ENWqVWP69OnccMMNzJ8/n6lTpzJ16lTA+g8866yzGDx4MFdffXWxvEFKNIBevXrRp08f3nrr\nLWbOnEnPnj2LpWncuDEjR44sVAYCtGrVir59+/LCCy8wY8aMuBWCs2bNirjIxk8//UTTpk2LhQ8Y\nMCDieXTo0CEwvEWLFgwdOpRBgwYxfvz4QIVgSkoKr732WqEyEOzCJvfffz/XX389M2bMKKIQfOqp\np9i7dy/XX389f/nLX4od77jjjuO4444LlMfP5s2bAXttwusiIyOjmC/If/zjH+Tn5zN06NBiykCg\nmEXi008/TX5+PjfccEMRZSDA9ddfz4QJE5gwYQIjR47khRdeKFFeRVEURVEURVGUpPDdeJgwEEy+\n3a+eCTd8CHWbJ7yoX7N/ZfS3oxm3Yhx5BXmF4WkpaVzT4hpuPeVWamYcWQthHkxUIagopaRVq1bM\nmzePOXPmMGXKFL766isWLlzIr7/+ypw5c5gzZw4ff/xxsRV8AbZt28bkyZNZsmQJO3fuJC/PDm6e\nH76VK1cGKgS7detGpUqVioV7C2Vs3Lgx7vNo0KBBMeVeTo790lO1atXAPOFKrXCys7P55JNPmD9/\nPlu3buXAgQNF5Fu5cmVgvmbNmgVO7410fp988glgFZRloUOHDrz44ovcd9995Ofnc/755wdaaALk\n5uYyY8aMuMqdNWsWAP369QuM79+/PxMmTCArKytu2RVFURRFURRFUQ4KC8bCpD8Dzr9/rWZw40dQ\ns0lCi8nOy+bNZW/y8ncvszd3b5G4C467gLtOv4vG1RsntMyjEVUIKkoZ6dSpE506dQKgoKCAuXPn\n8vDDD/Ppp58yduxYevbsWcQn4OjRo7n77rvZt29fxGPu3r07MNzzXRhO9erVAUq1sEjLli2LKS13\n7twJWOu8IKJZ3c2ePZtrrrmGDRs2REyTqPNbu3YtUPaVg2+66SamTZvGuHHjuOyyy0hLS6NNmzZ0\n7tyZa6+9lrPOOqsw7ZYtW8jOzqZixYqB1pNBeIrMZs2aBcafcMIJAFHrTFEURVEURVEUJWl8+Tz8\nxzcrq14ruGEiVDsmYUUUmAKmrJ7CyEUj2fTbpiJxp9c/nXva38Mp9U5JWHlHO6oQVEpHAqbd8u6N\nsOyj4qsLByEpcNJlh4wPwUikpKTQqVMnpk6dSocOHVi4cCETJ04sVAjOnz+f22+/nbS0NJ544gl6\n9epFZmYmlStXRkT461//yqOPPoqJsKKyt3BHsgmyUgTYu3cvvXv3ZuvWrQwcOJBbb72VE044gWrV\nqpGSksLUqVPp2bPnIXd+KSkpvPPOOwwdOpTJkyfzxRdfMGfOHEaOHMnIkSMZOHAgo0ePLvXxvfON\nNDU7Un0oiqIoiqIoiqIkFWPgsydg5j9DYce2hT9OgCp1ElbMlxu/ZPiC4SzfsbxIeNPqTbmr3V10\na9wt4vuUUjpUIagkj053wg+fhlYlikZaBnT8U/nLlCBSU1Pp1q0bCxcuZOvWrYXh48ePxxjD4MGD\nuffee4vlW7Vq1cEUM+FkZWWxdetWzjzzzEAFWqLPr0mTJvz444+sWLGCY44p+5epk08+mZNPPhmA\n/Px8pkyZwvXXX8+LL77I1VdfTbdu3ahfvz4ZGRlkZ2ezZs2amHwUNmrUiDVr1rB69erA9D/99FNh\nOkVRFEVRFEVRlEMCY2Da/8CckaGwJh3hunGQUfK6AsYYvtv2HWO+H8Pn6z/nQP4BKqZWpHNmZ/q1\n7kebum34YecPDF8wnC82fFEkb+2M2tx+6u1c8bsrSE9JT/SZKcChYW6kHJ00agctLoa0YGuzQtIq\n2XSNTj84csVALBZd3nTWzMzMwrAdO3YAdnGQcLZu3cq0adOKhR9ORDs/Ywxvv/12Qsu78MILAXj5\n5ZcTelywSt0//OEPXHLJJQB88803AKSnp9OtW7e4yu3SpQsAr7/+emD8a69Zy9euXbuWRWRFURRF\nURRFUZTEUFAAU+4uqgw8oRv88f2YlIG5Bbk88NkD3Pyfm5mxZgbZ+dkYDNn52UxfM53+/+nPxRMu\n5sqPriyiDMxIzeCWk29hyuVTuKblNaoMLEdUIagkDxG4fBS07Anple204CLxKTa8ZU+b7hAyD37+\n+ee56aab+Prrr4vF5eXl8dJLLzF+/HiAIisNe77uXn/9dfbuDTlH3bNnD/379y/03Xe44p3ftGnT\n+OGHHwrDCwoKeOihh5g7d25Cy7vnnnuoUqUKb7zxBo8//jj5+flF4tesWcPChQtLPM6YMWNYtGhR\nsfCtW7cWyuy37Pv/7N13eJXl/cfx93NOJishAjKCIsOwQUAQZFsVRUBAkDgYQhVbipbWUVGr1NHW\nihZqa38qgihLUAFRQSxhKlNkQxBQguwZyDw59++PJzlJIJCd5yT5vK7rXDn3/axP4KBXvrnHs88+\ni9vt5m9/+xvTp0+/5LqdO3eye/duX/uxxx7D7Xbz4YcfsmDBgmznzpw5k3nz5hEYGMjvfve7XLOK\niIiIiIgUqzQPfPYobJiS2df4LoieBUEVc73cGMP4leNZdnAZSWlJeMm+TJgXL0lpScSdj8Okb1Bi\nYXF3w7tZ2H8hY9uMpVJQzhtcStHRlGFxljsQBr4LhzbZv3mIXQKeRHtU4PW9oNMYeyShn0lNTWXq\n1KlMnTqVmjVr0rp1ayIiIjh16hRbtmzxbSLx5JNP+kaxgb15xZtvvsmmTZuoX78+nTt3xhjDihUr\nCAoK4qGHHmLKlCmXe6zfa9++PXfccQdffvklLVq0oEePHoSFhbFu3Tri4uJ44okneO2114rsefXr\n12fWrFkMGTKEp556ismTJ9OhQweMMezfv58ffviBF198kTZtrjy6dO7cuYwYMYLIyEhatWpFeHg4\nJ06cYOXKlSQkJNC9e3f69u3rO79jx4785z//4Te/+Q1Dhw5lwoQJtGnThuTkZPbu3cv27duZPn06\nUVFRALRp04Y33niDxx57jH79+nHTTTdRv359YmNjWb9+PW63m7fffjvHHZZFRERERERKjCcZ5o2E\nnQsz+1oMhrv/bf/8ngdbT2wlJi6GpLS8bXrZslpLnu/4PFERUQVJLAWkgqA4z7Igsi0MnuZ0kjwb\nOXIk9erVY+nSpaxbt46tW7dy7NgxAgMDiYyMZNiwYYwaNYrOnTtnu65q1aps2LCB5557jq+//ppF\nixZRo0YNBgwYwIQJEwq1cYW/+PTTT3n99df56KOPiImJoXLlynTs2JHZs2dz9uzZIi0IAtx1111s\n2bKF119/ncWLF/P5558THBxMZGQkv/nNb7jnnntyvccTTzxBgwYNWLNmDRs2bOD06dNUq1aNdu3a\nMXz4cO6//34CArL/5/LXv/417du3Z+LEiSxbtoxPP/2USpUqcc011/DUU09dMv33d7/7Ha1bt2bi\nxImsXr2aDRs2EBERwcCBA3niiSfo0KFDUf6xiIiIiIiI5E9KAsx+AH78JrOv7QjoPRHysQHktO3T\nSPYk5+lcC4talWqpGOgAS7tblg+WZcUA3bp160ZMTMwVz925cycATZo0KfZc4p8ypi6Hh4c7nERy\n4k//RufPnw9Av379HE4ipYE+L5JX+qxIfujzInmlz4rkR7n7vCSdgxn3ws9rMvs6joHbXsr38l03\nfnhjnkcHAoQGhLLu/kuX4yotmjdvzvbt2wGWG2O6OxwnzzRCUERERERERESkvEo4BR8OgF+yrKve\n/Rno9mSB1vJPTsvb6MAMSZ68Fw+l6KggKCIiIiIiIiJSHsUfhel3w7EdmX23vWyv519Awe7gfI0Q\nDAkIKfCzpOC0y7CIiIiIiIiISHlz5md4v1eWYqAFd71ZqGIgQNfIrrjyWG5y4aJrZNdCPU8KRgVB\nEREREREREZHy5OSPMOUOOLXPbltuGPAOtBtR6FsPazaM4IDgPJ0b5A5iWNNhhX6m5J8KgiIiIiIi\nIiIi5cXR7TClF5yLs9vuILh3OrQcVCS3b1GtBR1rdcz1vBB3CD3q9qB5teZF8lzJHxUERURERERE\nRETKg0Mb4f074cIxux1YAe6bDY17F9kjLMsiKiLqssdduHzFwJe7vIxVgI1LpPC0qYiIiIiIiIiI\nSFl3YDXMuBdS4u12cBW4bw5cm/tovvxI9aYyL3aer92iWgv2ntlLkieJkIAQukZ2ZXiz4RoZ6DAV\nBEVEREREREREyrLYpTD7AfAk2u3QCHjwE6h9Q5E/atnPyziWYI9AjAiJYGqvqQS5g4r8OVI4KgiK\niIiIiIiIiJRVO+bD3JHgTbXblWrC0M+gRpNiedzMXTN97++5/h4VA/2UCoIiIiIiIiIiImXRD7Pg\ns0fBeO122DUwbD5E1C+Wx8WejmXD0Q0AuC03g64vmo1KpOipICgiIiIiIiIiUtasfxcW/SGzfVVD\nGDofwiKL7ZGzds3yve95TU9qVqxZbM+SwtEuwyIiIiIiIiIiZcnqf2YvBl7dHEZ8WazFwHMp51i4\nb6GvHd04utieJYWnEYIiIiIiIiIiImWBMbDsZVjxWmZfnXZw/8dQIaJYH71g7wIS0zctaRjekHZX\ntyvW50nhqCAoIiIiIiIiIlLaGQOLn4Hv/p3ZV68LRM+E4MrF+miv8TJ792xfO7pxNJZlFeszpXA0\nZVikEHbt2sXo0aOJioqiQoUKhIaGcs0119CpUyf+8Ic/8PXXXzsd0VHx8fFMnDiRX/3qV9SqVYug\noCAqV65Ms2bNGDlyJEuXLr3kmnr16mFZFgcOHCj5wCIiIiIiIqWRNw0Wjs1eDGx4qz0ysJiLgQDf\n/fIdB84dAKBSYCXuqn9XsT9TCkcjBEUKaPbs2QwdOpSUlBTq1KlD9+7dqVq1KsePH2fTpk18++23\nLF++nFtvvdWxjFOnTmXEiBEMGzaMqVOnluizv/jiC4YOHcrJkycJCQmhffv21K5dm8TERHbu3MmU\nKVOYMmUK99xzDx9//HGJZhMRERERESkz0lLh00dg27zMvqb9YMC7EBBUIhFm7prpe393w7upEFih\nRJ4rBaeCoDjOGMPWE1uZun0qK+NWkpyWTLA7mK6RXRnebDjNqzX3u6HGR44c4aGHHiIlJYWJEycy\nduxY3G6377jX62XVqlWsWrXKwZTOWbBgAf3798cYw9NPP80zzzxD5crZfyu1Y8cOXnjhBWJjYx1K\nKSIiIiIiUsqlJsHHw2HPl5l9re+HPpPAXTIln7j4OJbHLfe17426t0SeK4WjgqA4KtWbyviV41l2\ncBkpaSl48QKQlJbE0p+WsvLQSrpHduflLi8T6Ap0OG2mzz//nISEBDp27Mjvf//7S467XC66du1K\n165dHUjnrJMnTzJ06FC8Xi8TJ07M8c8HoGnTpsyZM4cVK1aUcEIREREREZEyIPk8zLoP9mcW42j/\nMPT6G7hKboW4OXvmYDAAdKrdiXph9Urs2VJwWkNQHGOM8RUDk9KSfMXADF68JHoSWXZwGeNXjscY\n41DSSx07dgyAGjVq5PvatWvX8sQTT9CuXTuuvvpqgoKCqF27Nvfccw/fffddjte88MILWJbFCy+8\nwNGjR3nkkUeIjIwkODiY6667jqeffpqkpKRs19SrV48RI0YAMG3aNCzL8r2GDx/uO2/Hjh08//zz\ndOrUidq1axMUFETDhg0ZNGgQX331Vb6/v8mTJ3P27FlatWrF448/nuv5eS2a/vTTT7z66qv06NGD\nunXrEhwcTEREBD169GDGjBk5XhMTE4NlWXTv3p2EhASeffZZGjduTGhoKK1bt/adt3TpUn7729/S\nqlUrrrrqKoKDg7n22msZNmwYO3fuzNs3LiIiIiIiUlISz8D0/tmLgZ3HwR1/L9FiYJIniU9iP/G1\noxtHl9izpXA0QlAcs/XEVmLiYkhKS7rieUlpScTExbDtxDZaVG9RQumu7JprrgHgm2++Ydu2bTRv\n3jzP144fP56YmBiaNWtG+/btCQ4OZvfu3cybN4/PPvuMmTNnMmjQoByvPXjwIG3btsUYQ6dOnTh3\n7hyrVq3ib3/7Gzt27GDBggW+czMKjKtXr6ZBgwZ07tzZdyzr+4kTJ/Lee+/RpEkTWrVqRZUqVYiN\njWXp0qUsXbqU119/nXHjxuX5+5s/fz4AQ4cOLdKp3tOnT+e5556jQYMGNG7cmJtvvpm4uDhWrlxJ\nTEwMa9eu5Z///GeO1yYlJdG9e3d27txJ165dadWqFSkpKb7jo0ePJi4ujmbNmtGlSxcsy2Lbtm18\n8MEHzJ07l8WLF2f7MxMREREREXHMhRN2MfDIlsy+W56HLn8o8Shf7v+Ss8lnAahTqQ5d6nQp8QxS\nMCoIimOmbZ9Gsic5T+cme5KZtmMa/+j2j2JOlTf9+vWjdu3a/PLLL9xwww3cdtttdOvWjbZt29Ku\nXTvCwsIue+0f//hHPvroI66++ups/QsXLmTgwIGMHj2a3r17U6HCpYuwTpkyhVGjRvHWW28RFGQv\nDrtz507at2/PwoULWb16NTfffDMA//jHP5g6dSqrV6+mc+fOl91U5MEHH+TZZ5+lXr16vr4zZ86w\nYcMGBg4cyNNPP83gwYOJjIzM9c/F4/GwZYv9P6Ubb7wx1/Pz4/bbb6d///40a9YsW39sbCy33HIL\nkyZN4r777qNDhw6XXLt27Vpat27N3r17L/lzB/vPqnv37oSHh/v6jDH83//9H6NHj+bhhx9m+/bt\nfreWpYiIiIiIlDPnfoEP7oYTuzP77vg7dHikxKMYY7JtJjI4ajBul/sKV4g/UUFQCqTFtJIdqefF\ny+IDi1l8YHGh77V12NZC36Ny5cosXbqUoUOHsmHDBr744gu++OILwF4/8KabbmLs2LHce++li6n2\n6tUrx3v26dOHQYMGMWPGDJYtW0bv3r0vOadu3bpMmjTJVwwEaNKkCQ8++CD/+c9/+Oabb3wFwbzq\n1q1bjv3t2rVjzJgxvPLKK8yfP5/f/va3ud7r5MmTeL321O+CTKe+kssVGBs1asRzzz3Hww8/zNy5\nc3MsCAK89dZbORYDAe6+++5L+izL4pFHHuGDDz5gzZo17Nix45JipIiIiIiISIk5fQCm9YUzP9lt\nywV9J8MNDzgSZ8uJLew8ZS+xFOwOZkDDAY7kkIJRQVCkgJo0acL69etZs2YNixYtYu3atWzatInT\np0+zZs0a1qxZw5dffpnjyLwTJ07w+eefs23bNs6cOYPH4wFg27ZtAOzZsyfHgmDPnj0JDQ29pL9x\n48YA/PLLLwX6XuLj41m0aBGbN2/m1KlTnD9/HrDX7cvI4w+SkpJYvHgx69ev5/jx4yQn2yNMDx8+\nDFw+59VXX02nTp2ueO+4uDgWLVrErl27OHfuHGlpaYC9o3TGvVUQFBERERERRxzfAx/0g/j0n/lc\nATDwXWjW37FIWUcH3nHdHYSHhF/hbPE3KgiKFFKnTp18xSav18t3333Hiy++yJIlS5g2bRq9e/fO\ntibgf//7X8aNG0dCQsJl73nu3Lkc+zPWLrxYlSpVAC7ZWCQv5s+fz0MPPcSpU6fynediV111FS6X\nC6/Xy7Fjx4iKisp3nsv59ttvGTx4MHFxcZc953I5r7322ive+89//jOvvPKKrzCbn3uLiIiIiIgU\nq8Nb7DUDE07YbXcw3Dsdrr/dsUgnEk9km8GnzURKHxUEpUCKYtrtH2L+wNKfll6yu3BOXLi4td6t\nfrOG4OW4XC46derEF198Qfv27dm0aROfffaZryC4YcMGHn30UQICAnjttdfo06cPkZGRVKhQAcuy\neOaZZ3j11Vcvu6Oyq4h3i4qLiyM6OprExESefvpp7rvvPurVq0dqaioul4s5c+bwyCOP5HmH54CA\nAFq2bMnmzZtZv349XboUzYKyCQkJ9O/fn6NHjzJy5EgeffRRGjZsSOXKlXG5XCxZsoTbb7/9sjlz\nGlWZYd68eUyYMIHKlSszceJEevbsSa1atXzX3HfffcycOdOvdrkWERERESkXjIFDG2HNJIhdAqlJ\nEBgCjW6HTmOhThso6+t8H1wHH90DSfbGHQRWhPtmwXVdHY01b888PF57QEXL6i1pelVTR/NI/pXc\nXtQiFxnWbBjBAcF5OjfIHcSwpsOKOVHRcbvd9OzZE4Djx4/7+ufOnYsxhrFjx/LHP/6RqKgoKlas\n6NusYu/evSWa8/PPPycxMZGBAwfy6quv0qJFC1+RraB5+vbtC8AHH3xQZDlXrFjB0aNHadu2Le++\n+y5t27YlLCysUDkzfPzxxwC88sorjBo1ivr162crIJb034mIiIiIiABpqTBvFEzrAzsXQmoiYOyv\nOxfAtLvs42mpTictPvuW2xuIZBQDQ8Jg6HzHi4Eer4c5e+b42hodWDqpICiOaVGtBd0juxPiDrni\neSHuEHrU7UHzas1LKFnu8jJa7OeffwbItjtvxrTcunXrXnL+8ePH+frrr4sooS1j85HLTYW9Up7k\n5GTmzZuX72eOGTOGKlWq8MMPP/Dmm2/mev7KlStzPedKOQFmzJiRv5B5vPfOnTv5/vvvC3xvERER\nEREpAGPg09GwexGkJoC5aFaZ8dr9uxbZ55XW2TzGQNwGmDMUXq4JL4TbX+cMgzX/gg/vgdQL9rkV\nqsHwRVA3580WS9Kyg8s4lnAMgIiQCG679jaHE0lBlPmCoGVZUZZlPWZZ1oeWZe2yLMtrWZaxLOue\nAtwr0LKsWyzLet2yrO8syzpsWVaKZVmHLMuaa1lW9ytceyD9ucayrFdzec5HWc6NyW/O0sKyLF7u\n8jI96vYgNCAU10UfRxcuXzHw5S4v+0bR+YN///vfjBgxgnXr1l1yzOPx8M477zB37lyAbDsNZ2z+\n8cEHH/g27gB7U4+HHnqIM2fOFGnOOnXqAHZhKycZeebNm8fRo0d9/SkpKTz11FPs27cv38+sXr06\nU6dOxbIsxo0bxzPPPEN8fPwl5+3Zs4fo6GjGjh2b6z0zcv7vf/9j165dvn6v18uECRNYvXp1vnNe\nfO933nmHlJQUX/+xY8cYNmzYFdcVFBERERGRYnBoI+z+In1U4BV4Eu3zDm0qmVxF6UojIHfMhyXj\nwZv+80nl2jDiS6jZwtHIGbJuJnLP9fcQ5A5yMI0UVHlYQ/BR4LEiulc3IGMI1xFgI3ABaAoMBAZa\nlvUXY8zzudxnqGVZzxpj0i4+YFlWFcC5bYJKWKArkL91/RvbTmxj6vaprDy0kiRPEiEBIXSN7Mrw\nZsP9amRghtTUVKZOncrUqVOpWbMmrVu3JiIiglOnTrFlyxbfbr9PPvkkt9+eudDriBEjePPNN9m0\naRP169enc+fOGGNYsWIFQUFBPPTQQ0yZMqXIct50003UrFmTTZs20a5dO5o1a0ZgYCA333wzI0aM\noG/fvtxwww18//33NGrUiO7duxMSEsKqVas4d+4cY8eOZdKkSfl+bv/+/fnss88YPnw4r776Km+8\n8QYdOnSgdu3aJCYmsnv3bl+RcsiQIbner02bNvTp04eFCxfSunVrevToQVhYGOvXr+fnn3/mgMWj\nPwAAIABJREFUySef5O9//3u+cwI8/vjjfPDBByxatIiGDRvSoUMHEhMTWb58OXXr1uXuu+/ms88+\nK9C9RURERESkANZMBk8eN0z0JMG3k2HQ1GKNVKSyjYDMqeiZZcRjYEUY8QVEXFdi8a4k9nQs64+s\nB8BtuRl0/aBcrhB/VR4KgtuA14AN2AW897ALewXhBeYB/zTGZJvnaFnWvcBHwHOWZS0zxiy7zD02\nAO2AW4Gvcjg+BAgF1gPOjwUuAZZl0aJ6C17v/rrTUfJs5MiR1KtXj6VLl7Ju3Tq2bt3KsWPHCAwM\nJDIykmHDhjFq1Cg6d+6c7bqqVauyYcMGnnvuOb7++msWLVpEjRo1GDBgABMmTOC///1vkeYMDg7m\nq6++Yvz48Xz77bd8//33eL1ePB4PI0aMICAggOXLl/PSSy/x2WefsWTJEqpWrUqnTp14+umn2b59\ne4Gf3bdvX/bv388777zDF198wfbt21mzZg1BQUFce+21jBo1ivvvv5/u3bvn6X5z587ljTfeYPr0\n6cTExFCpUiU6duzIjBkzSExMLHBBsH79+mzatInx48ezatUqFi5cSJ06dXj44Yd5/vnneeyxovp9\ngoiIiIiI5Ens4kunCV+O8cL2T2HPYggIgcAKEBh60avClY8FhkJA9r6whP2kuYLgzM/Zj7nchf/+\n8joCMuP7SzjlNwXBWbtm+d73vKYnNSvWdDCNFIZV3nbOTJ+C2w0YZIyZW8T3fhcYCUwxxoy86NgB\n4FpgDPAvYLYx5pKhUZZlrQHaA78HJgHLjTHdiyBbDNCtW7duxMTEXPHcjJFbTZo0KexjpZTKmLoc\nHh7ucBLJiT/9G50/fz4A/fr1cziJlAb6vEhe6bMi+aHPi+SVPiulyAvhZBsl50/cwfZOx77iYkax\nMTSHguNljq1/D+LWk6fv0XJB07th0PvF/q3lJj4lnls+voVEj13InHL7FG6sWS7GMV1R8+bNMwbT\nFEn9pqSUhxGCJSlj94HIK5yzFtgB9LMsK9wY41s0zrKsKKAj8AVwuNhSioiIiIiIiPij1ERwBYDX\nT3cPTku2Xxk7/xY344U9OU0uLHkLflzgKwY2DG9Iu6vbOZxICkMFwaLVKP1rbsW8qcDfgWjgP1n6\nR6R/db70LyIiIiIiIlJSvF7Y+jF8MyF/xUDLBU36wd1v2cVE3yvB/uq5uC8p81hqgr0Goa+d+Tpz\n4jBubwqVQwKyn+8ETx6mFhczr/Fmmy48JGqIX238KfmnKcNFd9+awC4gDOhrjFl40fED2FOGbwTi\n0l8bjTEd0o+7gZ+BEKAW0Bf4mCsMObUsazgwPI8RWwNhzZo14+WXX77iiTVq1KBy5cpERl5poKOI\nOCUuLo74+HiOHTvmdBQRERERkUKrFr+DZodmEZ54IN/XeqwgVjf6E2cqNij6YBczBpdJxe1NSX8l\n4/am4jbJWfqyHkvBbS7uS6XO6e9wkcc1EgGPK4hFrd4txm8sd7GpsUy7MA2AYIJ5MuxJgq1gRzP5\ni/Hjx2vKcHllWVYA8CF2MfCbi4uBFzPGHLEs6yugt2VZTYwxO4HbgNrAv4wxKXmstNej4BukiIiI\niIiIiDimcuIhmv4ym5rnNmfrT3ZXIiGoGpWTfiHApFz2eo8VxJGwNpypUL+4o9osC68VhNcVRGEm\nNLuMh1pn1uPKwxqCXiyOVLmhEE8rGmuT1/retwlqo2JgGaCCYNF4G7gFOAg8kMdrpgK9sUf4PUXm\nSL+p+XjuAWB5Hs9tDYRVq1Yt10V0MzYs0IYS5Zc2FfFvhw8fJjw8nI4dOzodRYtzS77o8yJ5pc+K\n5Ic+L5JX+qz4kfijEPMq7J6WfTfhgBDo+FuCb36c4MBQ+HS0vRuvJyn7eZYLAkIIiLqTyP5vE+kO\nLPKIxfp5iasD0/rkaQqyKzCUyHteJTKybdHnyKND5w/x3LznfO0/3fknrgvzj12P/cH48eOdjlAg\nKggWkmVZ/8TeWfgIcIsx5kgeL10AnAQetCzrNaAfsNUYszGvzzbGTCWPBcQsU6VFRERERERESl7K\nBfj2LVj1JqReyHLAglbR0PNZCKuT2T3wXTi0CdZMgtgl9lp6AaFwfS/oNAbqOFckK5Q6bSHqTti1\n6MrrAwaE2ufVaVNy2XIwe/dsTPpoxo61OqoYWEaoIFgIlmW9DowFjmMXA2Pzem36tOAZwO+wNxEJ\nRpuJiEguytu6ryIiIiJSBnjTYPMMWPYyxF+0B2f97nDrX6BWy0uvsyyIbAuDp5VEypJjWdD/7VxH\nQBJ1p32eg5t3JHmS+CT2E187unG0Y1mkaKkgWECWZf0dGIc9yu9WY8yOAtxmKnZB8C7AA3xUZAEL\nwbIsjDF4vV5cLpfTcUQki4yCoHb0EhEREZFSYe9SWPI8HNuevb9GU7sQ2PAWRwtejnEHlooRkF8d\n+IqzyWcBqF2xNl0juzqcSIqKCoIFYFnWX4EngNPYxcAfCnIfY8wmy7JWAU2AJcYYv9gyNDAwkJSU\nFJKSkqhQoYLTcUQki6SkJMD+dyoiIiIi4reObIUlz8G+Zdn7K9WEnuOh9f3gcjuTzV/4+QhIYwwz\nds7wte9tfC/u8v53VoaoIJgDy7JeBfoDnxpj/nTRsb9gbwJyBrsY+H1hnmWM6VKY64tD5cqVOXny\nJKdPnyY0NFQjkUT8hDGG06dPA/a/UxERERERv3P2kD01ePMMyLqLbmBFuPkxe+RbUEXH4knebTmx\nhZ2n7E1Hg93BDGg4wOFEUpTKfEHQsqw2wL+zdDVN//qKZVl/zOg0xtyU5ZxaQFT616z36gs8m97c\nC/zuMsWyXcaYvxYyumOqVKnCqVOnOHfuHABVq1YlJCQEy7JUHBQpYcYYjDEkJSVx+vRpzp07h2VZ\nhIWFOR1NRERERCRTcry9Wci3b2XfKMNyQZuh0P1PULmmc/kk32bumul736teL8JDwh1MI0WtzBcE\ngSpAhxz6GxXgXhFZ3rdLf+VkOVBqC4IhISFERkYSFxfHuXPnfIVBKT/S0tIAOHz4cC5nSkmzLIvI\nyEiCg4OdjiIiIiIiAmke2DQVYv4KF45nP9boNrh1AtRo4kg0KbgTiSdYcmCJrx3dRJuJlDVlviBo\njIkB8jWszRgzHBieQ/9U7I1ACpKjXj7Pn0s+cxelSpUqcd1113H27Fni4+NJTU3V7qblSHx8PADh\n4foNkD+wLIvAwEAqV65MWFiYioEiIiIi4jxjYPeXsPTPcGJP9mM1W8BtL9k7CEup9EnsJ6R6UwFo\nWb0lza5q5nAiKWplviAoBRccHEyNGjWoUaOG01GkhM2fPx+Ajh07OpxERERERET8zqFN9oYhP63K\n3l+lDtzyPLQYDC6XM9mk0DxeD7N3z/a1oxtrdGBZpIKgiIiIiIiIiOTu9E/wv7/A1o+z9wdVhi7j\n4KZHITDUmWxSZJYdXMaxhGMARIREcNu1tzmcSIqDCoIiIiIiIiIicnmJZ2Dl67D2v5CWnNnvCoB2\nD0G3p6BiNefySZGatWuW7/3ARgMJcgc5mEaKiwqCIiIiIiIiInIpTwpseA+W/w0ST2c/1vgu+NWL\nUK2hM9mkWOw9vZd1R9YB4LbcDI4a7HAiKS4qCIqIiIiIiIhIJmNgx3xY+gKc3p/9WJ22cNvLcK3W\nGy+LZu3OHB3Y85qe1KxY08E0UpxUEBQRERERERER28F1sHg8xK3L3h9+Lfzqz9BsAFiWM9mkWMWn\nxLPgxwW+9pCoIQ6mkeKmgqCIiIiIiIhIeXfyR/jmRXtkYFYh4dD1CWj/awgIdiablIgFPy4g0ZMI\nQIOwBtxY80aHE0lxUkFQREREREREpLxKOAXL/w7r3wVvama/OwjaPwxd/gAVIpzLJyXCa7zZNhOJ\nbhyNpZGgZZoKgiIiIiIiIiLlTWoSrPsvrHgdks9mP9ZsANzyPERc50w2KXHfHf6OA+cOAFApsBJ9\nGvRxNpAUOxUERURERERERMoLrxe2zYNvJsDZn7Mfu6Yj3PYSRLZzJps4Zuaumb73fRv0pUJgBQfT\nSElQQVBERERERESkPNi/EpY8C4c3Z++PaAC3ToDGvbVhSDl06PwhVsSt8LXvbXyvg2mkpKggKCIi\nIiIiIlIaGQOHNsKaSRC7xJ4GHBgCjW6HTmOhThu7wHd8N3z9Z9jzZfbrK1wF3Z6GdiPAHejM9yCO\nm7N7Dl7jBaBjrY7UD6vvcCIpCSoIioiIiIiIiJQ2aanw6WjY/QV4kiC9oENqIuxcYBcIG/SECtXg\n++lg0jKvDQiBmx6Fzr+HkDBn8otfSPIk8UnsJ752dONoB9NISVJBUERERERERKQ0MSa9GLjILgBe\nctwLqQmw6/NLj7UcAj2fhfC6xZ9T/N5XB77iTPIZAGpXrE3XyK4OJ5KSooKgiIiIiIiISGlyaKM9\nMjCnYuDlXNcVbv0L1G5dfLmkVDHGZNtMZHDUYNwut4OJpCSpICgiIiIiIiJlU5Y19nrv/BK3SYFt\noZeusVfarJ5kTxPOq2s6wdAFpfN7lWKz9cRWdpzcAUCQK4gBjQY4nEhKkgqCIiIiIiIiUvZctMZe\nQE5r7EXdCf3fdm5DDU8KJJ2BxNOZr4RT2duXvM5A8tn8PefwZhUD5RJZRwfecd0dVA2p6mAaKWkq\nCIqIiIiIiEjZkuc19hbZ5w18t3AFs9TEXIp4ORT1Ek9DyvmCPzM/PPmYWizlwsnEkyw+sNjX1mYi\n5Y8KgiIiIiIiIlK25HWNPU+ifd6hTfb04ZQL+SvoJaaP5svP9F0nBIQ6nUD8zCexn5DqTQWgZbWW\nNKvWzOFEUtJUEBQREREREZGyZc3kvBfpUhPg/TvsUYPpBZISY7kgtGqWV8RF7Ytf4fbXzx+HnQvt\nzHl5xvW9iv97kVLD4/Uwe/dsX3tI4yEOphGnqCAoIiIiIiIipV+aB07tg2M7YNfneSuW+a5NLtyz\nXYFQ4XLFvPDLF/iCKoPLlf/ndRoLsV/bxczcBIRAxzH5f4aUWTEHYziacBSAiJAIbq93u8OJxAkq\nCIqIiIiIiEjp4fXCmQNwbJdd/Du2E47vghN7IC2lcPcOCM17MS+0amYRMLBCyW7aUaetvSHKrkVX\nXh8wINQ+r06bkssmfi/rZiIDGw0kyB3kYBpxigqCIiIiIiIi4n+MgbNxdrHv2I7MAuCJPXkbGZcf\nAaHw1H4ILCVr7VmWvTtyll2Us42ItFz2yMCMXZS1w7Ck+/HMj6w7sg4Al+VicNRghxOJU1QQFBER\nEREREecYA+eP2iP9ju2E4+lfj+2ClPj83atKHaje2B5BeHIfYHK/xnJB1B2lpxiYwR1o7458aBOs\nmQSxS+zRggGh9pqBncbYIwlFssg6OrBn3Z7UrFjTwTTiJBUERUREREREpGRcOJml4JelAJh4On/3\nqVgDajSGGk3tAmCNplA9yp7iCxC3Aab1Kftr7FkWRLaFwdOcTiKlQHxKPAt+XOBrRzeOdjCNOE0F\nQRERERERkfLMGDi0MXOUWWoSBIZAo9vtzSvqtMn/lNOks/YIv4uLfxeO5e8+IeF2sa9Gk8xX9SZQ\n8aorX6c19kQuseDHBSSm/3toENaAG2ve6HAicZIKgiIiIiIiIuVVWmrO69ClJsLOBXaBMGMdOnfg\npdenXEhf4y99fb/ju+zC37lD+csRVCm92Jc+2i9j9F+lqwu2/p3W2BPJxhjDrF2zfO0hjYdg6XNf\nrqkgKCIiIiIiUh4Zk14wW2QXAC857rWn3O5aBPNGQZc/ZBb8ju20C4BnfiZP6/RlCAi1p/ZmHe1X\nowmERRZ9Ue6iNfY8u77A7U3FCtQae1L+fHf4Ow6cOwBAxcCK9GnQx9lA4jgVBEVERERERMqjQxvt\n0XM5FQOz8iTCjs/sV165AqHa9emFv8aZ037DrwWXu3C58yPLGnuL5s8HoF+/fiX3fBE/kXUzkX4N\n+lExsKKDacQfqCAoIiIiIiJSHq2ZbE+lLQzLDVc1yD7ar0YTiKif8xRjESlxv5z/heVxy33texvf\n62Aa8RcqCIqIiIiIiJRHsYuzr6uXKwui7she/KvWCAKCiy2iiBTenN1z8Kb/W7+p1k3UD6vvcCLx\nByoIioiIiIiIlEep+RwdaAHRM3M9TUT8R3JaMvNi5/na0Y2jHUwj/sTldAARERERERFxQGBI/s4P\nCC2eHCJSbL7a/xVnks8AUKtiLbpFdnM4kfgLFQRFRERERETKowa/yvu5lsvemVdESpWsm4ncG3Uv\n7pLc1Ef8mgqCIiIiIiIi5Y0xkByf9/MDQqDjmOLLIyJFbuvxrWw/uR2AIFcQAxoNcDiR+BMVBEVE\nRERERMqbVRNhf0zezg0Ihag7oU6bYo0kIkUr6+jAXtf1ompIVQfTiL9RQVBERERERKQ82f0lfPOX\nzHbV+hBYwZ4WnJXlsvsb94b+b4NllWxOESmwk4kn+erAV772fY3vczCN+CPtMiwiIiIiIlJeHNsF\n834NGLt9bWd48FM4shXWTILYJeBJtEcFXt8LOo2BOm0djSwi+fdJ7CekelMBaFmtJc2qNXM4kfgb\nFQRFRERERETKg8TTMCsaUtLXDgy7BgZPg4AgiGxrvxeRUs/j9TBnzxxfe0jjIQ6mEX+lKcMiIiIi\nIiJlXZoHPh4Bp/bZ7cAKED0DKlZzNpeIFLnlB5dz5MIRACJCIrit3m0OJxJ/pIKgiIiIiIhIWff1\n87BvWWa7/9tQs4VzeUSk2GTdTGRgo4EEu4MdTCP+SgVBERERERGRsmzzDPjurcx21yehaT/n8ohI\nsfnxzI+sPbIWAJflYnDUYIcTib9SQVBERERERKSsOrgeFj6W2W58F3T/k3N5RKRYZR0d2KNuD2pW\nrOlgGvFnKgiKiIiIiIiURed+gdn3Q1qK3a7exJ4q7NKPgSJl0fmU8yz8caGvHd042sE04u/0fwIR\nEREREZGyJjURZt0P54/a7dCqED0Tgis7m0tEis2CHxeQ4EkAoH5YfdrXbO9wIvFnKgiKiIiIiIiU\nJcbY04R/2WS3LTcMmgYR1zmbS0SKjTEm23Th6MbRWJblYCLxdyoIioiIiIiIlCXf/gu2zM5s93oV\n6ndzLo+IFLvvDn/HgXMHAKgYWJE+Dfo4G0j8ngqCIiIiIiIiZcXepfD185ntGx6E9g87l0dESsSs\nXbN87/s26EvFwIoOppHSQAVBERERERGRsuDEXvj4ITBeu123A/R+HTRtUKRM++X8L8TExfjaQxoP\ncS6MlBoqCIqIiIiIiJR2SWdh5hBIPmu3q9SBez+EgGBnc4lIsZuzew7e9F8E3FTrJuqH1Xc4kZQG\nKgiKiIiIiIiUZt40mDcKTsba7YAQGPIRVKrhbC4RKXbJacnMi53na0c3jnYwjZQmKgiKiIiIiIiU\nZt9MgNglme1+b0HtG5zLIyIlZvGBxZxJPgNArYq16BapDYQkb1QQFBERERERKa22fAyr38xs3/w4\ntLjHuTwiUqJm7pzpez84ajBul9vBNFKaqCAoIiIiIiJSGh3aBAvGZLYb3Q63PH/580WkTNl6fCvb\nTm4DIMgVxIBGAxxOJKWJCoIiIiIiIiKlTfxRmHU/eJLsdrXrYeA7oNFBIuXGzF2ZowN7XdeLiJAI\nB9NIaaOCoIiIiIiISGniSYbZD0D8L3Y7OAyGzISQMGdziUiJOZV0iq8OfOVrazMRyS8VBEVERERE\nREoLY2DROIhbZ7ctFwyaAtUaOptLRErUJ7GfkOpNBaBFtRY0r9bc4URS2qggKCIiIiIiUlqs/S98\n/2Fm+9YJ0PBXzuURkRLn8XqYvXu2r63RgVIQKgiKiIiIiIiUBvtiYPEzme2WQ6DjmMueLiJl0/KD\nyzly4QgAESER3FbvNocTSWmkgqCIiIiIiIi/O7UP5gwDk2a367SFPv8Ey3I2l4iUuJm7MzcTGdBo\nAMHuYAfTSGmlgqCIiIiIiIg/S46HmfdB0hm7Xakm3PsRBIY4m0tESty+M/tYe3gtAC7LxeDrBzuc\nSEorFQRFRERERET8ldcLnzwCx3fabXcwDPkIqtRyNpeIOGLmrszRgT3q9qBWJf23QApGBUERERER\nERF/FfMq7F6U2e7zT4hs51weEXHM+ZTzLPhxga+tzUSkMFQQFBERERER8UfbP4UVf89s3/RbaK0C\ngEh5tXDfQhI8CQDUD6tP+5rtHU4kpZkKgiIiIiIiIv7m8Bb47DeZ7fo94NYJzuUREUcZY7JNFx7S\neAiWNhWSQlBBUERERERExJ9cOAGz7odUeyQQEfVh0PvgDnA2l4g4Zu2Rtew/ux+AioEV6dugr8OJ\npLRTQVBERERERMRfeFJgzlA4+7PdDqoM0bMgtKqzuUTEUTN3Zo4O7NugLxUDKzqYRsoCFQRFRERE\nRET8xVdPwU+r0xsWDHwXqkc5GklEnHX4/GFi4mJ87SFRQ5wLI2WGCoIiIiIiIiL+YP17sGFKZvuW\n5yCql3N5RMQvzNkzB6/xAtChVgfqh9d3OJGUBSoIioiIiIiIOO3AKvjyycx284HQeZxzeUTELySn\nJTNvzzxfO7qxdhqXoqGCoIiIiIiIiJNO/2SvG+j12O1araDvv0A7iIqUe4sPLOZ08mkAalWsRbfI\nbg4nkrJCBUERERERERGnpFyAWfdBwkm7XbE6DJkBQRWczSUifmHWrlm+94OjBhPg0m7jUjRUEBQR\nEREREXGCMfDZo3B0m912BcK9H0JYpLO5RMQvbD2+la0ntgIQ6ApkQKMBDieSskQFQRERERERESes\n+AfsmJ/Z7v0PuOYm5/KIiF+ZtTtzdOAd191BREiEg2mkrFFBUEREREREpKTtWgTLXsps3/hraDvc\nsTgi4l9OJZ3iy/1f+traTESKWoEmn1uWVQPoDHQAagHVgFDgJHAC2AWsBL43Jn1vbBEREREREYGj\nO+CThzPb9bpAr1edyyMifueT2E9I9aYC0Pyq5jSv1tzhRJKVMYbNB8/wzsp9/BhfOjeAynNB0LKs\nCOABYATQMuuhi041Wd5fsCxrLjDFGLOqwClFRERERETKgoRTMCsaUs7b7fBrYNA0cAc6m0tE/IbH\n62HO7jm+dnQTjQ70J6lpXsbN2czSHcdI9qRhTO7X+KNcC4KWZdUG/gwMBYLILAD+DPyAPSLwNJAE\nVE1/1QNaA5WA4cAwy7J2AS8YYz4u0u9ARERERESkNEjzwMfD4fQBux1YEaJnQcWrnEwlIn5medxy\nDl84DEDV4KrcXu92hxNJBmMM4+Zs5usdR0lKLd0TYq9YELQs62/AGOzpwBeAT4A5wGpjzIlcrnUD\nLYC7gCFAU2CWZVlPAqONMRsLH19ERERERKSUWDIe9i/PbA/4L1zdzLk8IuKXZu6a6Xs/8PqBBLuD\nHUwjWW0+eIalO46V+mIg5L6pyBPAIWAUUMMYc78xZn5uxUAAY0yaMWazMeYlY0xz7GnGHwKtgN6F\nDS4iIiIiIlJqbJoOa9/ObHf/EzTp41weEfFL+87sY+3htQC4LBeDrx/scCLJ6t2V+0n2pDkdo0jk\nNmX4AWBWUWwMYozZhj11+M9AncLeT0REREREpFT4eS18/vvMdpO+0PVJ5/KIiN+atXuW7333yO7U\nqlTLwTRysf/tOoa3lK4ZeLErFgSNMTOK+oHGmAPAgaK+r4iIiIiIiN85GwezH4D03UK5ujnc/R9w\n5TZZS0TKm/Mp55m/d76vrc1E/E9SatkYHQj52GVYRERERERE8iE1EWbdDxeO2e3QCBjyEQRXcjaX\niPgFYwxbT2xl6vaprIxbSVJaku9Y7Yq1aX91ewfTSU5CAt0klpGioH4tJSIiIiIiUtSMgflj4PBm\nu+0KgMEfQNV6jsYSEf+Q6k3lqRVPMXLxSL756ZtsxUCA44nHeXrl06RmjC4Wv9CzcQ0sp0MUkSIt\nCFqWFWZZ1gOWZT1tWdZDlmXVLsr7i4iIiIiIlAqr34RtczPbvf4K13VxLo+I+A1jDONXjmfZwWUk\npSXh5dJtG1K9qSw7uIzxK8djTBlZtK6UM8ZQKSSAsvK3kecpw5ZlNQL6APHGmHdyON4Z+BSIyNKd\nYlnWOGPMfwqdVEREREREpDTYsxiWvpjZbjscbhzlWBwR8S9bT2wlJi7mklGBF0tKSyImLoZtJ7bR\nonqLEkonOfF6DX9ZtIPZ6w86HaXI5GeE4AjgNaDtxQcsy6oCzAOuAo5hFwa3A8HAZMuyuhY+qoiI\niIiIiJ87vgfmjYKMMSTXdIQ7XgOrrEwyE5HCmrZ9Gsme5Dydm+xJZtqOacWcSK7Ek+blyXlbeH/1\nAV9f9UrBhAa6cZXi/7TnpyCYUdSbksOxXwPVgR+BZsaYe4wxLYH30p/x+0KlFBERERER8XeJZ2BW\nNCSfs9tVImHwdAgIcjaXiPiVFXErcpwmnBMvXlbErSjmRHI5yZ40xsz4nrkb43x9d7aoyaqnejDz\n4Zu4o3mtUlsUzE9B8BrAA2zM4djd2L8Ce8UYcypL/xPp19xU4IQiIiIiIiL+zpsG80bCyb12OyAU\nomdAperO5hIRv5OclrfRgRmSPFeeWizFIyHFw6hpG/hq+xFf3+B2kUyObkNwoJvWdcN56/421K9c\nOlcVvOIagpZlDc3SvBpIBu63Lh3unjGNuPpF1wAcBa6+qH+zMWZLAfKKiIiIiIj4n6V/hr1LM9t3\nvwW1WjmXR0T8VrA7ONf1A7MKCQgpxjSSk7MJqTw0bT0bfzrt6xvV+TrG925CDjWxUim3TUVGZHkf\nCKRd1AdQFQgB4oE7crhHBcB90XXvAyoIioiIiIhI6ffDLFgzObPd5Q/QfKBzeUTEr7Wo1oL1R9fn\n6VwXLrpGaluGknQ8PpkH31vLriPxvr4/3Ho9Y3o2LDPFQMilIGiM6ZHx3rKss0AocKcxJjFL/2+B\nycBXxph7L76HZVmrgVZZ7yUiIiIiIlImxG2EBWMz29ffAT2edS6PiPi1T2M/ZdOxTXnP6bEtAAAg\nAElEQVQ+P8gdxLCmw4oxkWQVdzqBB99bx/4TF3x9L/RpyvCbr3MwVfHIzxqCu7BH+o28qH849vqB\nSy5zXSPgUL6TiYiIiIiI+LP4IzD7fshYD6x6Yxjwf+DKz49ZIlIepHnTeG39azy/5nnSTFqerglx\nh9Cjbg+aV2tezOkEYO+x8wx6+1tfMdDtsnh9UKsyWQyE3KcMZzUTuBGYaFlWI2A30Bd7/cBTwOyL\nL7AsqxVQDfim8FFFREREREQcYAwc2ghrJkHsEkhNgoAQ+5WUvr5USDgMmQEhVZzNKiJ+Jz4lnidX\nPMmqQ6t8fY3CG1GnUh3WHllLsic5267DLlwEuYPoUbcHL3d5uUxNU/VX2w6dZdiUdZy8kAJAkNvF\n5Ptu4PZmNR1OVnzyUxCcDNwJ/AoYk95nYa8r+Igx5nwO14zGHj24qDAhRUREREREHJGWCp+Oht1f\ngCcJTPoP7Z5E+5Vh4DtwVQNnMoqI3zp47iBj/jeGfWf3+fp61O3BX7v8ldCAULad2MbU7VNZeWgl\nSZ4kQgJC6BrZleHNhmtkYAlZf+AUD72/nvhkDwAVgtz834Pt6NyomsPJileeC4LGmDTLsu4AHsDe\nPKQqsB94xxiz8eLzLcsKA+oD/wPmFU1cERERERGREmJMejFwEaQmXv48VyD8MBsa3goaySMi6dYd\nXse45eM4m3zW1/frFr9mzA1jcFn20gItqrfg9e6vOxWx3IvZfYzRH24kKdX+ZU+VkADeH9GettdW\ndThZ8cvPCEGMMWnAtPRXbueeBW4vYC4RERERERFnHdpojwy8UjEQwJtqn3doE0S2LZlsIuLX5uye\nw6trX8Vj7FFnQa4gJtw8gd71ezucTDIs2nKYx2d/T2qaAaBapWCmj2xPk1rlY+kHrXYrIiIiIiKS\nkzWT7WnCeeFJgm//Vbx5RMTvebweXln7Cn/57i++YmC10Gq83+t9FQP9yOz1P/O7mZt8xcA64aHM\nHd2x3BQDIZ8jBEVERERERMqN2MWZawbmxnhhz1fFm0dE/NrZ5LP8cfkf+e7wd76+JhFNmNRzEjUr\nlt3NKUqbd1bs4+UvdvraDapX5MNRHagVFupgqpKngqCIiIiIiEhOUvM4OjCDJ5epxSJSZu0/u5/f\n/e93/HTuJ1/fbdfexkudXyI0oHwVmvyVMYaJX+9h8v/2+vqa16nCtBHtuapSsIPJnKGCoIiIiIiI\nSE4CQ3JfPzAr/dAvUi6tObSGPy7/I/Gp8b6+37T6DaNbjcbSRkN+wes1vLhwO9O+zSzYtq8XwbvD\n21ElJNDBZM5RQVBERERERCQnDW+DnfPzdq7lgut7FW8eEfErxhhm7JrB39f/HW/68gIh7hBe6vwS\nt9fTHqv+wpPm5cm5W/jk+0O+vu5R1fnP/W0JDXI7mMxZKgiKiIiIiIhczOvN5+jAEOg4pvjyiIhf\nSU1L5ZV1rzB3z1xfX40KNZjcczJNr2rqYDLJKik1jbEzv2fJjqO+vrta1mLi4NYEBZTvfXZVEBQR\nEREREcnKGPj6Odi7JG/nB4RC1J1Qp03x5hIRv3A66TTjYsax4egGX1/Lai15s8ebVK9Q3cFkktWF\nZA+//mADa3486euLbn8NL93dHLdLU7lVEBQREREREclq1Rvw7b8y2+H14MIx8CRl33XYctkjA6Pu\nhP5vg9YKEynz9p7ey5j/jeHQ+czpp73r9+bFTi8S7C5/G1P4qzMJKQx/fz2bD57x9T3StT5P39FY\n6zqmU0FQREREREQkw8ap8M2Lme3Gd8E9U+HIFlgzCWKX2LsJB4TaawZ2GgN12jqVVkRK0Iq4FTy5\n4kkupF7w9T3W5jFGNh+pIpMfOXYuiQffW8fuo5mbvDxxexS/6d5Af09ZqCAoIiIiIiICsGM+fP77\nzHa9LjDwPQgIhMi2MHiac9lExDHGGKZtn8bEjRMxGABCA0L5a5e/0vOang6nk6wOnkrggffW8tPJ\nBF/fX/o148GO9ZwL5acKXBC0LCscuAtoDlQFrrRPszHGjCzoswrDsqwooBdwI9AOuB6wgEHGmLlX\nujaX+94HPAq0BNzALuB94D/GZJ1H4Ds/BuiW3pxljIm+wr1fBp5Jb/5kjKlX0JwiIiIiIpIH+2Jg\n3qjMKcG1WsGQGRAY4mgsEXFWSloKL377Igt+XODrq12xNpN6TiIqIsrBZHKxvcfieeDddRw5lwSA\n22Xxj0Et6X9DpMPJ/FOBCoKWZY0FXgUy/u+Y25hLAzhSEMQu2j1WlDe0LOst4DdAEvANkArcAvwL\nuMWyrEHGmLQr3OJuy7LCjTFnLj5gWZYLeLAo84qIiIiIyBUc2gSz7oe0FLt9VUO4fx6EVHE2l4g4\n6rz3PCMXj2Tz8c2+vhtq3MAb3d/gqtCrHEwmF9sSd4ZhU9ZxOiEVgKAAF2/d14Zbm17tcDL/le+C\noGVZQ4A305vHgcXAIezimD/aBrwGbAA2Au+ROVIv3yzLGohdDDwCdDXGxKb3Xw0sA/oDY4B/XuYW\nG7BHKg4B3s7h+K+AusB67FGNIiIiIiJSXI7vgY/ugZTzdrtybXjwU6iknUJFyrPDaYf58PyHnD13\n1td3d8O7ee6m5whyBzmYTC723b6TjJq2gfPJHgAqBrl5Z1g7OjWo5nAy/1aQEYIZo+0+BoYaY5KL\nME+RM8a8m7VdBAtI/in961MZxcD05xy1LOtRIAZ42rKsyTlNHQamAzcAw8m5IDg8/etUVBAUERER\nESk+Z+Ngen9IOGm3Q6vaxcDwa5zNJSKO+ubnb3gn/h1SsEcNuywX49qOY2jTodqUws/8b9dRHv1w\nE8keu/wSXiGQqSPa07puuMPJ/J+rANc0x54CPMbfi4FFzbKsSKAtkIJdEM3GGLMce7RkTeCmy9zm\nF2AJ0MGyrCYX3T8Me4ThDmBd0SUXEREREZFsLpy0i4Hn4ux2YAW472Oo0djZXCLiGGMM72x5h8eX\nPe4rBlYKrMTknpMZ1myYioF+ZsEPv/DwBxt9xcAalYOZ/XBHFQPzqCAFQQ9w1hhzvKjDlAI3pH/d\nboxJvMw56y86Nyfvp38dflF/NPa6jO8jIiIiIiLFIzneniZ8Yo/ddgXCvdOhriboiJRXSZ4knlr5\nFJO+n+Trq+qqyod3fkjXyK4OJpOcfLT2Jx6b9T0er73rc92IUD4e3ZGompUdTlZ6FKQguBmo/P/s\n3Xd81dX9x/HXudkQNoQZZe8NioAgiOIuuK2jouKvbltHa4uigrjqqNpKrahgaetGVESGgKTiBNEQ\nhmxCkEDYIze54/z++N5cEggZlyTfjPfz8fBx+Z7v+d68oxfUTz7nHGNMTdxht03odXMRc7YcNbcw\nHwG7gWuNMVH5xscAAWB6pAFFRERERKQI/hx4+1rYtiw0YOCSV6D9Wa7GEhH37Dy8kxvn3MjsjbPD\nY22i23Br4q20q9/OxWRSmH98sZ5xM1ZgnVogHZISee+WQZzcqLa7waqYSPYQfA7nUI7bcU4arkkS\nQ6+HipgT2o2Y45alrbU5xpj/4vw9HAnMNsZ0BgYAn1hrt4eWJxfJGDOGY7sMj6c3QFZWFjNnzizh\nI1LT6bMiJaXPipSGPi9SUvqsSGmU6PNig/Tf9DIt9x7ZnefHVr9h0/poWK/PW02hP1skvwx/Bv8+\n9G/22/3hsVNiT+HChAuJMlH6vFQi1sIn6R7mZxzpbUuubbnhpL18vXCOi8mqplIXBK21HxtjxgOP\nGmMs8EIRy2erm7wNA2wZvNdUnILgGGA2cENovDTLhVtzAicmi4iIiIjUGNbSM31agWLgquaXsKnJ\nCBdDiYibUnNT+eDwB/jwAeDBw/kJ5zMgdoD2C6xkghbe2+jhy8wjxcD2dYPc3ClIfCStblL6gqAx\nZkHolweBScBDxpiVwIEiHrPW2urwb9q87zGxiDl594r6+4G19ntjTCowyhjTBLgO2AV8Uoo8m4Av\nSji3N1CvcePGjBo1qhRfQmqivJ+C6bMixdFnRUpDnxcpKX1WpDRK/HlZ8BjsWnjkesAtdDn3Sbro\nf/prDP3ZInmCNsjkHyfz9o9vh8fqxNbhmTOeYVCLQYA+L5WJLxDkvnd/5MvMbeGxs7ok8ber+xIf\nE1XEkxVj3LhxbkeISCR11GFHXSfgnLxblLLoqKsMNoVeTy5iTvJRc4syFXgWeB1oDrxorc0taRhr\n7dTQexTLGLMIdROKiIiISE301cuw+C9HrntcAec8ASoGitQ4h32HefDLB5m3eV54rHXd1rx05ku0\nrtfavWBSKK8vwB3/Wcb8VTvCY6N6t+CZy3sRExXJsRiSJ5KC4A3FT6m2fgi9djPGJBxnqfQpR80t\nynTgKeDC0PXUE4snIiIiIiIF/PgWzPnTkev2Z8Pol8Gj/5EUqWm2H9rOXQvuYtXuVeGxQS0G8fTQ\np6kXV8/FZFKYgzl+xk77jq837A6PXTPgJCaO6o7Hox/onKhI9hCcVh5BqgJrbboxZhnQF7gceDP/\nfWPMGUArYDvwVQneb4cx5l2cg0VWWWtLUkQUEREREZGS+HkOfHjbkevkAXDFmxAV414mEXHFjzt/\n5O4Fd7PLuys8dk2Xa7iv/31Ee7QJXWWz51AuY974lh+37guP3TasHfef00n7O5YR/VisEMaYJ4wx\nq40xhZ2inDf2lDGmfb5nkoCXQ5dPWmuDJfla1tqrrbWNrbVDTiy1iIiIiIiEbf4K3vkN2IBzndQV\nrn4bYmu5m0tEKtzH6z/mxs9uDBcDo0004weO54FTH1AxsBLK3O/lile+KlAM/OO5nfnDuZ1VDCxD\n1f6Tb4zpy5FCHUDX0Ovjxpj78gattaflm9Mc6BR6LcBa+54xZjJwK5BqjJkP+IARQF3gQ+BvZfpN\niIiIiIhIyW1fAf+5Evxe57r+yXDtB5DQwN1cIlKhgjbIi8te5LUVr4XH6sXV4/lhz3NKs1OKeFLc\nsmXXYa557WvSdzs7tBkDj43uzjUDijrKQSJRZEHQGDM09MvD1trvjxorFWvt4kieKwN1gQGFjHeI\n9A2ttbcZY/4H3I5zUEcUsBrncJDJJe0OFBERERGRMrZ7A0y/BHJCnSW1k+C6GVD3mJ/1i0g1dsh3\niAdSHmBR+qLwWLt67XhpxEsk10k+/oPimjXbD3Dda9+w40AOANEew7NX9GJU75YuJ6ueiusQXIRz\nQvAajnTW5Y2Vhi3B1yoX1tpFQKl6Sq21Y4Axxcz5D/CfUrznsFJm+J5S5hYRERERqdEObId/XQwH\nM53ruLpw7fvQqJ27uUSkQmUczODOBXeyds/a8NiQlkN4eujTJMYmuphMjmd5+l7GvPEtew/7AIiL\n9jD52r6c2bmpy8mqr+KKdFtwinnbChkTERERERGpHLL3wvRLYc8m5zo6Hn79FjTv6WosEalYyzKX\n8buFv2NPzp7w2JhuY/hd398R5YlyMZkcz5L1Wdw87XsO5Tp7vibGRTPl+v6c1raRy8mqtyILgtba\n1iUZExERERERcUtUMAf+exVkrnAGTBRc9ga0HuxuMBGpUDPWzmDC1xPwB/0AxHhiGD9wPKPbj3Y5\nmRzPvJWZ3P6fZeT6nZ3XGtSKYdqNp9KzVX2Xk1V/1f5QERERERERqb6M9dN/499h//Ijg6P+Dp3P\ndy+UiFSoQDDAc0uf482Vb4bHGsY35K/D/0qfpD4uJpOifPhDBve++yOBoLMItWndOKbfNIAOTeu4\nnKxmUEFQRERERESqpmCQPpun0Cx/MXDkJOj9a/cyiUiFOpB7gPsX38+XGV+Gxzo26MhLZ75Ei8QW\nLiYTay3L0/fyasoGFq7eidcXID4mijM7J9GqQTyvLN4Ynntyo1pMv2kAyQ1ruZi4ZlFBUERERERE\nqh5rYe44kvcsOTJ2+j0w6A73MolIhdqyfwt3LriTDfs2hMfOTD6TJ4Y8Qa0YFZbc5AsEueed5cxf\nuYMcf4BQEyDZvgCfpv5S4GCKTk3r8K+bTiWpbrwrWWuqIguCxpiFwARr7cKy+GLGmAbA74Esa+2L\nZfGeIiIiIiJSA6U8C1+/fOS67/UwYrx7eUSkzFlrSc1KZWraVFK2ppATyCEuKo6hrYZySrNTeOmH\nl9ifuz88/+YeN3NHnzvwGI+LqcVayz3vLGfeyky8vuCx9/P9un5CDG/93wAa1I6ruIACFN8hOASY\nb4z5GvgnMMNau7+YZ45hjBkI/BoYA9QGHirte4iIiIiIiADw/euwYGL4clu9/rS48HkwxsVQIlKW\nfEEf41LGsTB9IbmBXII4hSVvwMu8zfOYu3lueG6sJ5YJgydwQdsL3Ior+SxP38v8lTsKLQYeLccf\nZPPubBUEXVBc2bw7MBMYCLwOZBpjPjLG/NEYc5YxJskYU+DcbmNMojGmhzHmBmPMy8aYjcD/gDuA\ng8DtwFNl/62IiIiIiEi1l/YhfHJP+HJnYleWtr4VPFFFPCQiVYm1NlwM9Aa84WJg+H6+HrPYqFje\nOOcNFQMrkSkpG8nxB0o0N8cfYErKhuInSpkrskPQWrsauMQYMwC4B/gVcCFQ4HeaMeYgkAvUA/L/\nmzjvR3Q/A68Ck621h8smuoiIiIiI1CjrF8D7YwkvOGvRh28b30LQE+NqLBEpW6lZqSzaughvwFvs\nXA8ejLqDK5UFq3eE9wwsTtDC56t2lG8gKVSJFtZba7+x1l4JtATuBj4F9uAU/AxQB2iEU2A0OP+G\nTgVeBs6w1na21j6rYqCIiIiIiERk61J461oI+pzrRh3gmvfwRyW4m0tEyty0tGnk+HNKNDc3kMu0\nldPKOZGUhtdXsu7A8PwSdhNK2SrVKcPW2t3AS6G/MMa0B5oDjYE4YDeQBay11h4o26giIiIiIlIj\n7VwD/74MfIec67ot4boZULuxu7lEpFws3rr4mGXCxxMkyOKti8s5kZRGfEwU2aUoCsZHa8sHN5Sq\nIHg0a+06YF0ZZRERERERESlobzr862LI3u1cJzR0ioH1k93NJSLlwuv3lmip8NHPSOVxZuckZq/4\npUTLhj0GRnRJKv9QcgydxS0iIiIiIpXToSynGLg/w7mOqQ3XvAdNOrmbS0TK3IHcA0xJncK5759b\n6mfjo+PLIZFEauyQNsRGl6zcFBcdxdghbcs5kRTmhDoERUREREREykXOAWeZ8K61zrUnBq76N7Tq\n524uESlTu7J38e9V/+at1W9xwFf6ncc8eBjaamg5JJNI9U6uT5PEONL3ZBc5Lz7Gw1ldk+jVql4F\nJZP8VBAUEREREZHKxZ8Db10N234IDRi49FVoN9zVWCJSdrYd3MbUtKl8sPYDcgIFDxBpGNeQA74D\n+PIOESpCbFQs13e9vrxiSgR+2rqvQDEw7+TZPB7jdAae1TWJ567orVOiXaKCoIiIiIiIVB7BALw/\nFjbmOyTgwueg28XuZRKRMrN+73peX/E6n274FL/1F7jXum5rbux+Ixe0uYAHv3yQhekLi9xPMD4q\nnuHJw+neuHt5x5YSCgYtj3ycFr4+pXVDkurEsWD1Drz+APHRUYzoksTNQ9rSK7m+i0lFBUERERER\nEakcrIVPfg+rPjoyduaD0P9G9zKJSJlI3ZnKlNQpLEhfcMy9Lg27MLbHWEacNIIoj3Pi7KQhkxiX\nMo5FWxeR488pcOqwBw+xUbEMTx7OpCGT1GFWiXy4PIMftuwFIDbKwzOX9+TkRrVdTiWFUUFQRERE\nREQqhwUTYdm0I9en3QZD7nMvj4icEGst32z/himpU/jml2+Oud+/aX9u7nEzA1sMPKaoF+OJ4amh\nT7EiawVT06aSkpGC1+8lPjqeoa2GMqbbGHUGVjIHc/w8OXt1+PqmIW1UDKzEVBAUERERERH3Lfkb\npDx75LrnlTByEqjzR6TKCdogC9MXMuWnKazYteKY+8NaDeOmHjfRO6l3ke9jjKFHkx48O+zZIudJ\n5fD3hevYccDZDzKpThy3D2/vciIpigqCIiIiIiLiruX/hbnjjlx3OAdG/R08HvcyiUip+YI+Zm+c\nzeupr7N+3/oC9zzGw3ltzuPG7jfSsUFHlxJKedmUdYjXUjaGrx84rzOJcSo5VWb6pyMiIiIiIu5Z\nMxtm3n7k+qSBcPlUiIpxLZKIlI7X7+WDtR8wLW0a2w5tK3Av1hPL6PajGdN9DMl1kl1KKOXtsVmr\nyA04+zz2Oak+o3u3dDmRFCfigqBxFvhfDJwNJAMJ1toR+e7XBvoB1lqbcqJBRURERESkmtn0Jbw7\nBmzAuW7aHX79FsTWcjWWiJTM/tz9vL36baavms5u7+4C92rH1OaKTldwXZfraFKriUsJpSIs/nkn\n81dlhq8fuagbHo+2e6jsIioIGmM6AB8AXYG8f8r2qGleYArQzhhzirV2WcQpRURERESkevnlJ/jv\nVeD3OtcNWsO170NCfVdjiUjxsrKzmL5yOm+veZuDvoMF7jWIa8C1Xa/lyk5XUi+unksJpaL4AkEm\nfLIyfH15v1b0Staf41VBqQuCxpgGwHycrsAfgfeA+4E6+edZawPGmJeB54BLARUERUREREQEdm+A\n6ZdCzn7nunYSXDcD6jRzN5eIFCnjYAZvrHiDD9d9SE4gp8C9ZrWbMabbGC7pcAkJ0QkuJZSK9uZX\nm1m3wykKJ8ZFc/+5nVxOJCUVSYfgvTjFwNnAKGut3xhzB0cVBEM+xikIngWMK+S+iIiIiIjUJAe2\nw5uj4dAO5zquHlz3ATRs624uETmudXvW8dqK15i9cTaBvCX+IW3qteHG7jdyQZsLiNHenzXKroM5\n/HX+z+Hru0a0J6lOvIuJpDQiKQiOwlkefJ+11l/URGvtemNMDqCzpkVEREREarrsPfCvS2DvZuc6\nOh6ufhua9XA3l4gU6qedPzEldQoL0xcec69bo26M7TGWM086E4/RieA10TNz13DA65SF2jauzZhB\nbVxOJKURSUGwDZBtrV1VwvkHAW0cICIiIiJSk+Uehv9cBTvSnGsTBVe8CScPdDeXiBRgreWrX77i\ntdTX+Hb7t8fcP7XZqdzU4yYGNh+Ic9ao1EQrMvbx1nfp4euHLuxKbLQKw1VJJAVBW9LnjDGxOMXA\n/RF8HRERERERqQ4CPnj3ekj/+sjY6Jeh4znuZRKRAoI2yIItC5iSOoW0XWnH3B+ePJyxPcbSs0lP\nF9JJZWKt5ZGP0rCho2WHd2rC8M5J7oaSUoukILgR6GaM6WCtXVvM3PNDX6Ok3YQiIiIiIlKdBIPw\n4W2wdu6RsXOegF5XuZdJRMJ8QR+zNszi9RWvs3HfxgL3okwU57c5nxu730j7BtoJTBwf/biN7zfv\nASAmyvDQhV1dTiSRiKQgOAvojnO4yC3Hm2SMaQI8g9NRODOidCIiIiIiUnVZC3P+BKnvHBkbch8M\nvM29TCICQLY/mw/WfsDUtKlsP7S9wL1YTywXd7iYG7rfQMvEli4llMrocK6fJz5dHb6+YXAb2jZJ\ndDGRRCqSguCzwP8BNxtjDgPP579pjEkCLgEeBFoAGcDkE8wpIiIiIiKVlbWQsRSWvOh0Avq8EBMP\n9VvDznyLhfrdAGc+6FpMEYH9uft5a/VbTF85nT05ewrcS4xJ5MpOV3Jt12tpnNDYpYRSmU1etJ7t\n+70ANE6M484z1TlaVZW6IGitzTLGjAI+Bu4O/QWAMSYLaJB3CewGRltrD5VBVhERERERqWwCPphx\nC6z5FPxesEFn3JddsBjY5SK44FnQIQQiZcZaS2pWKlPTppKyNYWcQA5xUXEMbTWUMd3G0L1x9/DB\nH1nZWby58k3eWfMOh3wF/xe9YXxDru1yLVd2vpK6sXXd+FakCtiy6zCvLN4Qvv7DuZ2oEx/jYiI5\nEZF0CGKt/Z8xphfwOHAZEBu61TD06gfeBx6w1m4+4ZQiIiIiIlL5WBsqBs5yCoDHYzzgiXZeRaRM\n+II+xqWMY2H6QnIDuQRxivHegJf5m+eTkpHCsFbDuLX3rUxfOZ0P131IbjC3wHs0r92cMd3GcHGH\ni0mITnDj25AqZNKnK8n1O5+zXq3qcVnfVi4nkhMRUUEQwFq7BbjWGDMW6A80BzxAJvC9tfZg2UQU\nEREREZFKKWOp0xlYVDEQnK7Bn+dAxjJo1a9isolUY9bacDHQG/Aecz9IkGx/NnM2z2H2ptnH3G9b\nry039biJ89qcR4xHHV5SvC/XZTEnLTN8/fCvuuHxqOO7Kou4IJjHWusF/lcGWUREREREpCpZ8pKz\nTLgk/F746m9w+Rvlm0mkBkjNSmXR1kWFFgPzC+Yt4Q/p3qg7Y3uOZXjycDzq2JUS8geCPPpxWvj6\nkj4t6XtSgyKekKrghAuCIiIiIiJSQ62dc2TPwOLYIPz8WfnmEakhpqVNI8efU+L5DeMb8tTQpxjQ\nbEB4T0GRkpr+9WZ+znQWgdaKjeKP53V2OZGUhVL/SMAY09cYs8AY85cSzH0hNLdXZPFERERERKTS\n8pWwOzCPv5ilxSJSIl9s/SK8Z2BJZPuzOa35aSoGSqntPpTLc/N+Dl/feWYHmtaNdzGRlJVIOgSv\nB84AXi3B3BXAncBvgHsj+FoiIiIiIlIZHdrlHBJiAyV/RocWiERk+6HtfLf9O5ZmLmVp5lJyAiXv\nDgTwlnRpv8hRnp27hv1ePwCtG9XixtNbuxtIykwkBcHhodcFJZj7MfAKcGYEX0dERERERCqjlR/B\nrHtKVww0Huh4bvllEqkmrLVsObAlXPxbmrmUjIMZJ/Se8dHq6JLSW7ltP//9dkv4+sELuhIXHeVi\nIilLkRQEk4Fsa21mcROttduNMdmhZ0REREREpCo7tAtm3w8r3i/9s9HxMPCOss8kUsUFbZD1e9cX\nKADuzN5Z5DMGg8WW6P09eBjaamhZRJUaxFrLIx+nEQx9zIZ2bMKILknuhpIyFUlBMAZKsVkBBIBa\nEXwdERERERGpLPK6Ag/lK1QkNoNG7SBjWdH7A0YnQKfzoWXf8s8pUsn5g37W7OnSejUAACAASURB\nVF7D95nfszRzKct2LGNfzr4in0mITqBXk170a9qP/k37Y4zh1vm3kl2CfTljo2K5vuv1ZRVfaohZ\nqb/w7cbdAER7DOMv7Ko9KKuZSAqCGUB7Y0wna+2aoiYaYzoBicDGSMKJiIiIiIjLjtcV2PsaOOdx\niK0NM26BNZ+C31vw1GHjcToDO50PF/8D9D+TUgPlBnJJ25XG0sylfJ/5Pct3LOeQ71CRz9SJqUPf\npn3p17Qf/Zr2o0ujLsR4YsL3rbUMazWMhekL8QaOvz9gfFQ8w5OH071x9zL7fqT6y84N8PisVeHr\n6we1pn1SoouJpDxEUhBcCHQAHgWuKmbuBMCGnhERERERkaqksK7AOs3hoheh48gjY5dOcboEl7wI\na+c63YLRCc6egYPugJb9Kj67iEuy/dn8tPOncAHwp50/FXsISMP4huHiX7+m/ehQvwNRnuPv1WaM\nYdKQSYxLGceirYvI8ecUOHXYg4fYqFiGJw9n0pBJ6uySUvnHF+vZts8pNDeqHctdIzq4nEjKQyQF\nwb8CNwGXG2N8wB+stb/kn2CMaQ78BbgcZ8nwX080qIiIiIiIVJAiuwInQUKDguPGQKt+cMW0isso\nUkkcyD3A8h3Lw0uA03al4Q/6i3wmqVYS/Zv2d5YAN+tPm7ptSl20i/HE8NTQp1iRtYKpaVNJyUjB\n6/cSHx3P0FZDGdNtjDoDpdS27jnMP75YH76+/5xO1EuIKeIJqapKXRC01q42xtwDvABcDVxpjPkR\nyDt65mSgJ5D344z7rbUryiKsiIiIiIiUs1Ufwye/L6Qr8AXoeI57uUQqiT3ePSzLXBYuAK7Zs4ag\nLXqb/ZPqnFSgA7BlYssy6dozxtCjSQ+eHfbsCb+XCMATn64mx+98nru3rMvl/XVGbHUVSYcg1tqX\njDHbgeeAlkC/0F/5ZQD3WmvfObGIIiIiIiJS7krbFShSBVhrSc1KZWraVBbtXYQPH49Nf6xAB11x\nhbkdh3cUOAF43d51xX7d9vXbh4t/fZP60rR207L6lkTKzVfrdzEr9cgC0Ecu6kaUR8vNq6uICoIA\n1tp3jTEzgBHAaUBTwADbga+Bz621RfdJi4iIiIiI+9QVKNWQL+hjXMo4FqYvJDeQG95jzxvwMn/z\nfFIyUhjWahiThkwKH9hhrSXjYEa4+Pd95vekH0gv8ut4jIdODTqFl//2TepLg3gV0KVq8QeCPPpx\nWvh6VO8W9G/d0MVEUt4iLggChAp+c0J/iYiIiIhIVaKuQKmmrLXhYmBhp/AGCZLtz2Zh+kLuXnA3\nZ7Q6g2U7lrE0cymZhzOLfO9oE023xt3CewD2TupNndg65fWtiFSI/36XzurtBwBIiInigfM6u5xI\nytsJFQRFRERERKSKUlegVGOpWaks2rqo0GJgft6Al5SMFFIyUo47Jy4qjl5NeoWXAPds0pOE6ISy\njizimr2Hc3l27prw9e3D29G8nj7j1Z0KgiIiIiIiNcnh3fDp/bDivYLjva6Gcx9XV6BUC9PSppHj\nz4no2doxtemd1Jv+TfvTv2l/ujbqSmxUbBknFKk8np/3M3sP+wBIbpjA2CFtXU4kFaHIgqAxZkHo\nl5uttTccNVYa1lo7IoLnRERERESkrKgrUGqIxVsXh/cMLAmP8XBvv3vp16wfnRp0Itqj3hmpGdZs\nP8D0b7aEr8ed35X4mCgXE0lFKe5PuWGh19WFjJWGjeAZEREREREpC+oKlBomJ1C67kBrLb/p9pty\nSiNSOVlrefTjNAJBp2QzuH0jzummE7FriuIKgjeEXvcVMiYiIiIiIpWdugKlBor2ROML+ko8Pz46\nvhzTiFROc9K2s2T9LgCiPIaHL+qGMcblVFJRiiwIWmunlWRMREREREQqGXUFSg2017uXJ759olTF\nQA8ehrYaWo6pRCofry/AY7NWha+vO+1kOjbVadk1Sak3RjDG/Cr0yyXW2qwyziMiIiIiIidKXYFS\nA83fPJ+JX09kt3d3qZ6LjYrl+q7Xl1Mqkcrpn4s3sHVPNgANasXw+7M6upxIKlokO6V+CPiBhmWc\nRURERERETsTh3TD7D5D6bsFxdQVKNbbbu5vHv3mcOZvmFBhvWbslWd6sIvcTjI+KZ3jycLo37l7e\nMUUqjW17s3l50brw9b0jO1GvVoyLicQNkRQEdwNYaw+WcRYREREREYnUqk9CXYE7jowlNnO6Ajud\n614ukXJirWXO5jk8/vXj7MnZEx5PSkji4UEPM7DFQMaljGPR1kXk+HMKnDrswUNsVCzDk4czacgk\n7ZsmNcoTs1fj9Tm/H7o0r8uvTz3J5UTihkgKgmnAIGNMXWvt/rIOJCIiIiIipXDcrsBfw7lPqCtQ\nqqWs7CwmfT2J+VvmFxi/uP3F3HfKfdSNrQvAU0OfYkXWCqamTWXh5oX48RMfHc/QVkMZ022MOgOl\nxvl2424+/nFb+PqRi7oS5VFBvCaKpCD4T2AIcCcwqWzjiIiIiIhIiakrUGoYay2zNs7iyW+fZF/O\nvvB401pNeWTQI5ze8vQC840x9GjSg2eHPcvMmTMBGDVqVIVmFqksAkHLIx+lha8v7NmcAW0buZhI\n3FTqgqC19t/GmFOBR40x8cDz1trS7doqIiIiIiKRU1eg1EA7D+9kwtcTWJS+qMD4ZR0v495+95IY\nm+hOMJEq4u3v0ln5i7PQMz7Gw5/P7+JyInFTJKcMLwj98jDwZ+CPxph1wE4gcJzHrLV2RGQRRURE\nREQkTF2BUsNYa/lo/Uc89d1THMg9EB5vUbsFjwx6hIEtBrqYTqRq2HfYxzNz14Svbz2jPS3qJ7iY\nSNwWyZLhYYW8R+fQX8djI/g6IiIiIiKSR12BUgNtP7SdCV9NICUjpcD4lZ2u5Pf9fk/tmNouJROp\nWv76+c/sPpQLQMv6Cfz2jLYuJxK3RVIQvKHMU4iIiIiIyPGtngUf/66QrsC/Qqfz3MslUk6stcxY\nN4O/fPcXDvoOhsdbJrZkwqAJnNr8VBfTiVQtazMP8OZXm8PX4y7oQnxMlIuJpDKIZA/BaeURRERE\nREREjqKuQKmBfjn4C4989QhLti0pMH5Nl2u4q89d1Iqp5VIykarHWsuET1YSCDoLN09r25Dzujdz\nOZVUBpF0CIqIiIiISHlTV6DUMNZa3v35XZ5b+hyHfIfC4yfVOYkJgyfQr2k/F9OJVE3zVmaSsjYL\nAI+Bhy/qhjHG5VRSGZS4IGiMiQNGA/2AusBe4BvgY2utv3ziiYiIiIjUMOoKlBoo42AGDy95mG9+\n+SY8ZjBc1/U67uhzBwnROvxApLS8vgCPzVoVvr5mwMl0aV7XxURSmZSoIGiMGQS8CxTWV7rJGDPa\nWptapslERERERGoadQVKDRO0Qd5e8zbPL32ebH92eLx13dZMHDyR3km9XUwnUrW99r+NbNl9GIB6\nCTHcc3ZHlxNJZVJsQdAY0xL4BKgHGCAIZAFNQtdtgE+NMd2ttfvKMauIiIiISNVkLWQshSUvwtq5\n4PNCTDx0OAcG3QUNWsNnD0DqOwWf63kVnPekugKlWkrfn874JeP5PvP78JjHeLi+2/Xc1us24qPj\nXUwnUrVt3+fl7wvXha/vHdmRBrVjXUwklU1JOgTvBuoDe0K/fsdam2uMiQfGAk8BLYCbgOfKK6iI\niIiISJUU8MGMW2DNp+D3gg06475sWPURrJkdmpdz5JnEpnDRC+oKlGopaIP8Z9V/ePGHFwt0Bbar\n144JgyfQs0lPF9OJVA9Pfbaaw7kBADo3q8PVp57kciKpbEpSEDwbsMBd1tp/5w1aa73A30KFwaeB\nkaggKCIiIiJyhLWhYuAspwB4zP1gwUIgOF2B5z4BtRpWTEaRCrR5/2bGfzmeZTuWhceiTBQ3dr+R\nW3rdQmyUOphETtTSzXuY8UNG+Hr8RV2JjvK4mEgqo5IUBNviFATfP879d3EKgm3LKpSIiIiISLWQ\nsdTpDCysGFiYkY/BoDvLN5OICwLBANNXTeelH14iJ18RvH399jx2+mN0a9TNxXQi1UcwaHn047Tw\n9XndmzGoXWMXE0llVZKCYB0gM9QReAxr7ebQkdW1yzKYiIiIiEiVt+QlZ5lwSRgPZCwrfp5IFbNh\n7wYeWvIQP+38KTwWbaIZ23Ms/9fj/4iJinExnUj18t7Srfy01TneIS7aw5/P7+JyIqmsSnTKME6H\nYHHMiQQREREREal21s45smdgcWwQfv6sfPOIVCB/0M+0tGm8vPxlcoO54fHODTszcfBEOjfs7GI6\nkepnv9fH03NWh69/O7QtyQ1ruZhIKrOSFgRFRERERKS0fCXsDszjL+HSYpFKbu2etYz/cjwrdq0I\nj0V7ovltz99yU4+biPGoK1CkrL30+VqyDjrF9+b14rllWDuXE0llVtKCYENjzIITmGOttSNKkUtE\nREREpOqyFlbPKv1z0Qlln0WkAvmCPt5Y8QaTf5yMP+gPj3dt1JWJgyfSsUFHF9OJVF/rdx7kjS83\nha//dH4XasWqB0yOr6Sfjlhg2AnMKcmSYxERERGRqm/r9zD3IdiypHTPGQ90PLd8MolUgDW71/DQ\nlw+xaveq8FiMJ4bbet/GmG5jiPaoOCFSXiZ+shJ/0Cm9nNq6IRf1bO5yIqnsSvIn8rRyTyEiIiIi\nUtXt3gifT4C0DyJ7PjoeBt5RtplEKoAv4GNK6hT++dM/8dsjXYE9Gvdg4uCJtKuvZYsi5WnB6kwW\nrdkJgDEw/qKuhA5/FTmuYguC1tobKiKIiIiIiEiVdHg3LH4Gvv0nBH1Hxj3R0H8s7M+AdfOL3h8w\nOgE6nQ8t+5Z/XpEytGrXKh788kF+3vNzeCzWE8sdfe7guq7XqStQpJzl+ANM/ORIV+5Vp5xE95b1\nXEwkVYX+dBYRERERiYQ/xykCLv4LePcVvNd1FIx4GBq1g4APZtwCaz4Fv7fgqcPG43QGdjofLv6H\n09ohUgXkBnJ55adXeC31NQI2EB7v1aQXEwZPoG29ti6mE6k53vhyExuzDgFQJz6a+0Zqn04pGRUE\nRURERERKIxh0lgV//ijs3VLwXvIAGPkYJJ96ZCwqBi6dAhnLYMmLsHau0y0YneDsGTjoDmjZr2K/\nB5ETkJaVxoNfPsi6vevCY/FR8dzZ506u6XINUZ4oF9OJ1Bw79nt56fO14evfn9WRRolxLiaSqkQF\nQRERERGRktr0Jcx9ELYtKzjesC2c9Qh0+VXhXX7GQKt+cIW255aqKyeQw+Tlk5maNrVAV2DfpL5M\nGDyBk+ue7GI6kZrnqc/WcCjX+b3YISmR6wbq96CUnAqCIiIiIiLF2fkzzH/YWfabX0JDGPYA9LsB\nomPdySZSAX7c+SPjvxzPhn0bwmMJ0Qnc3fduft3513iMx8V0IjXPD1v28P6yreHr8Rd1JSZKvw+l\n5FQQFBERERE5noM7YNGTsHQq5OuIIioOTrsVhtwD8dq8Xao2ay2pWalMTZtKytYUcgI5xEXFMbTV\nUK7ufDUL0xfyr1X/Iphv/8tTmp3Co4MeJblOsovJRWqmYNDyyMcrw9cjuzZlSIcmLiaSqkgFQRER\nERGRo+Uehq/+Dl/+FXIPFrzX8yo480Gor0KIVH2+oI9xKeNYmL6Q3EAuQZyinzfgZd7meczbPA+L\nDc+vFV2Le/rdw+WdLldXoIhLPvghgx/T9wIQG+3hwQu6upxIqiIVBEVERERE8gQDsPw/sHASHPil\n4L02Q+HsidCitzvZRMqYtTZcDPQGvMfez1cIBBjQbACPDn6UloktKyqiiBzlYI6fpz5bHb6+eUgb\nTmpUy8VEUlWpICgiIiIiArBuPswdDzvSCo436QJnT4AOZxd+YIhIFZWalcqirYsKLQYeLcYTw119\n71IxUMRlLy1Yy84DOQA0rRvHbcPau5xIqir1eIuIiIhIzbY9Fd4cDdMvLVgMTGwKF70It/wPOo5U\nMVCqnWlp08jx55RobiAY4M2Vb5ZzIhEpysasQ7z+v43h6z+d14Xacerzksic8CfHGGOARkAta+2W\nE48kIiIiIlIB9mXAgsfgx/9C/qWRMbVh8F0w8A6IS3Qtnkh5W7x1cXjPwOIECbJ46+JyTiQiRXns\nk5X4As6/r/qd3IBRvVu4nEiqsogLgsaYgcCfgOFALZz/iorOd78+8Gxo/HZrbcl+9CQiIiIiUp68\n+53DQr76O/jzLZU0Huj7Gxj2J6jTzL18IhUkJ1C6/0Xz+otfWiwi5WPRmh18vnoH4DSsP3JRN4w6\n1+UERFQQNMbcDvwViDreHGvtXmNMI+Ai4BPgw4gSioiIiIiUhYAPlk6FRU/C4ayC9zqcA2c/Ckld\nXIkm4oa4qLgS7R+YJz46vhzTiMjx5PqDTPhkZfj6in7J9GhVz8VEUh2Ueg9BY8ypwAtAAPgDkAxk\nHmf6G4ABLo00oIiIiIjICbEWVn0CL58Gn95XsBjYvBdc/zFc846KgVLjtK7busRzPXgY2mpo+YUR\nkeN686tNbNh5CIA6cdHcf24ndwNJtRBJh+A9OEW+h621zwBFtal+EXo9NYKvIyIiIiJyYrZ+D3Mf\nhC1fFRyvlwwjxkP3y8Cjc/ak5nlnzTus3rO6xPNjo2K5vuv15ZhIRAqz80AOL8xfG76++6wONE6M\nczGRVBeRFASHhF4nFzcxtGx4P9Aqgq8jIiIiIhKZ3Rvh80chbUbB8bh6MOQeGHALxGj5o9RMM9fN\nZOLXE8PXBoPNf7DOUeKj4hmePJzujbtXRDwRyeeZOWs4kOMHoG2T2vxmYGt3A0m1EUlBsDGw31q7\nv4TzLREsTRYRERERKbXDu2HxM/DtPyHoOzLuiYZTboah90PtRu7lE3HZpxs+ZfyS8eHrbg270Tyx\nOV9u+5Icf06BU4c9eIiNimV48nAmDZmkAwxEKljq1n28szQ9fD3+wq7ERqu8ImUjkoLgPqChMSau\nuJODjTHNgHrA1kjCiYiIiIiUiM/rFAFTngHvvoL3uo6CEQ9Do3buZBOpJOZtnsef//dngtYp+nVu\n2JlXRr5C3di6rMhawdS0qaRkpOD1e4mPjmdoq6GM6TZGnYEiLrDW8sjHadhQ8+6IzkkM65Tkbiip\nViIpCP4InAkMA+YUM/eW0Os3EXwdEREREZGiBYOQ9oGzPHjvloL3kgfAyMcgWdtZiyxKX8QfvvgD\nARsAoH399rxy9ivUi3NOKu3RpAfPDnvWzYgiks/M5dtYunkPADFRhgcv7OpyIqluIikIvgmMAJ4w\nxnxtrd1X2CRjzLXAOJwlw69HHlFEREREpBCb/uccGLLth4LjDdvCWY9Al1+BljiKsCRjCfcsuge/\ndfYha123Na+OfJWG8Q1dTiYihTmU4+eJ2avC1zee3oY2jWu7mEiqo0gKgtOB3+AUBZcaY6YB8QDG\nmAuBrsClQH+c04hnWGtnl01cEREREanxdv4M8x+GNZ8WHE9oCMMegH43QHSsO9lEKpnvtn/HXQvv\nwhfaU7NVYiumjJxC44TGLicTkeN5edE6Mvc7O7Q1qRPHnWd2cDmRVEelLghaa60x5mLgX8Ao4JF8\nt2eGXvN+FPsBTvFQREREROT4rIWMpbDkRS5YNZsomwsrEqDDOTDoLmjZFw7thEVPwNJpEFr2CEBU\nHJx2q3N6cHw9974HkUrmhx0/cPvnt5MTcAoLzWs357VzXqNp7aYuJxOR49my6zCvpmwMX//x3M4k\nxkXSyyVStIg+Vdbag8DFxpgRwBhgINAc5zThTOArYKq1trg9BkVERESkpgv4YMYtTsef30t06MAD\nfNmw6iNYOxcatIE9m8B3qOCzPa+CMx+E+skVHlukMkvdmcqt828l258NQFJCEq+NfI0WiS1cTiYi\neay1LE/fy6spG1i4eideXwBjIBg6SKRnq3pc0qeluyGl2jqhMrO19nPg8zLKIiIiIiI1jbWhYuAs\npwB4zP0g+A7DjrSC422GwtkToUXviskpUoWs3r2a387/LYdCBfRG8Y2Ycs4UkuuqcC5SWfgCQe55\nZznzV+4gxx8IFwHzThUGqBsfTcBaPGg/XCl7HrcDiIiIiEgNlrHU6QwsrBhYmPqt4ep34TcfqRgo\nUoi1e9Zy89ybOZB7AID6cfV5deSrtKnXxuVkIpLHWss97yxn3spMsn1HioFH+37zHu55ZznWHmeC\nyAlQQVBERERE3LPkJfB7SzjZOEXAjiN1erBIITbu28jNc29mb85eAOrE1uGfZ/+TDg10IIFIZbI8\nfS/zV+7A6wsWOc/rCzJ/5Q5+3LqvgpJJTVLkkmFjTJkdCGKtfbOs3ktEREREqomf5zjLgkvEOvsJ\nisgx0venM3bOWHZ5dwFQO6Y2r5z1Cl0adXE5mYgcbUrKRnL8geInAjn+AFNSNvC3q/uWcyqpaYrb\nQ3AqUFa9qSoIioiIiAjkHoafP4MV74O/hEuF85R2vkgNsO3gNm6aexM7sncAkBCdwOSzJtOjSQ+X\nk4lIYRas3nHcZcJHC1r4fNWO8g0kNVJxBcHFlF1BUERERERqqoAP1i+E1HedPQNzD0b2PtEJZZtL\npIrLPJTJTXNu4pdDvwAQFxXH3878G32S+ricTEQKs27HAbJ9JesOzOMtYTehSGkUWRC01g6roBwi\nIiIiUt0Eg7BlCaS+BytnQvbuE3s/44GO55ZNNpFqICs7i7Fzx7L14FYAYjwxvDD8BU5tfqrLyUQk\nv8z9Xj5avo0Pl2eQtm1/qZ+Pj44qh1RS0xXXISgiIiIiUnLWwrYfnOXAKz6AA9sKn9eoA/S4DBp3\ngpm3ge9w8e8dHQ8D7yjbvCJV1B7vHm6eezOb9m8CINpE8/yw5xnccrC7wUQEgP1eH5+lbufD5Rl8\ntWEXkR4U7DEwoktS2YYToZoXBI0xrYA/AiOBkwADpAOfA09bazcc57mrgVuBnkAUsBp4A5hs7fF3\nvTbGnAvcA/QH4oENwH+BZ6y1OYXMnwpcDzxqrX2kkPv1gFnA4FCGs621W0vwrYuIiIhUrJ1rnE7A\nFe/B7kL/EwvqtoLulziFwGY9nZOCrYXVn8DqWUXvDxidAJ3Oh5baVF1kX84+/m/e/7Fu7zoAokwU\nT5/xNGckn+FyMpGaLccfYNGancxcnsH8VTvI9R9bPoiN9tDvpPos3byX3EDxh2rFRUcxdkjb8ogr\nNVy1LQgaY/oAC4D6wFZgTuhWf+C3wDXGmHOstUuOeu7vwG2AF6dw6ANGAH8DRhhjLrfWHrOA3xjz\nB+ApIAAsAvYAZwCPARcaY0ZYa0vwo+/w+zUJZe4DLAPOsdZmlfR5ERERkXK3d4vTCZj6PmSmFj6n\nVmPoNhq6XwbJA8DjKXjfGLj4HzDjFmdvQb+34KnDxuN0BnY635lnTPl9PyJVwMHcg9wy7xZW714N\ngMHw+OmPc/bJZ7ucTKRmCgYt327azczlGcz66Rf2e/3HzDEGBrZtxOjeLTm3RzPqxEVz11s/MG9l\nJl7f8YuC8TEezuqaRK9W9crzW5AaKuKCYKj77gac7rUWQG2cDrzCWGttu0i/VoT+jlMMfBW43Vrr\nAzDGxAD/AG4EJgO98h4wxlyKUwzcDgy11q4NjTcFFgIXA3cAL+T/QsaY/sCTwGHgTGvtN6HxRJwO\nv6HAJOD3JQlujEkG5gGdgBTgQmtt6TcaEBERESlrB3fCyg+dbsD0rwufE1sHulwEPS6FNsMgqpj/\n5IyKgUunQMYyWPIi/tWfEhX0YWISnD0DB90BLfuV+bciUtUc9h3mts9vY8WuFeGxCYMncH7b811M\nJVIzrfplPx8uz+Dj5dvYts9b6JxuLeoyundLLurVgmb14gvce+6K3tzzznLmr9xBjj9Q4NRhj3E6\nA8/qmsRzV/TG6IdhUg4iKggaY64B/omzLPa4RcB89yr0pGJjTDwwMHQ5Pq8YCGCt9RljHsIpCPY0\nxtTK17n3p9DrH/OKgaFnMo0xt+J0/j1gjHnpqKXDD+B8r0/lFQNDzx00xtwArAVuM8Y8aq3dW0z2\nDjjFwJOB2cCl1toi1s+IiIiIlDPvPlj1ibMceMMXcOxiCYiKg47nOMuBO4yEmFKeBmwMtOoHV0xj\n1syZAIwaNaoMwotUD9n+bO5YcAc/7PghPPbQaQ8xuv1oF1OJ1CwZe7Odw0F+yGBN5oFC57RqkMDo\n3i0Z3acF7ZPqHPe9YqI8vHhVH37cuo9XF29gweodeP0B4qOjGNEliZuHtKVXcv3y+lZESl8QNMb0\nxdlPLxp4HfgYmAHsBq4AmgJnAVcDB4DfARlllLekAoA/lLGwgmVegfIQkA3hjsd+QC7w7jEPWPuF\nMSYDaAmcBiwJPRcLnBea9u9CnttgjPkKp5PyfOA/xwttjOkJzMX5e/gOcG3+YqaIiIhIhfFlw8+f\nOZ2Aa+dB4JjtkMFEQbvhznLgzhdAfN2KzylSA+QEcvjdwt/x3fbvwmN/POWPXNHpChdTidQMew/n\n8mnocJBvN+4udE6DWjFc0LM5o3u3pN/JDUrc0WeMoXdyff5+jfbHlYoXSYfgPaHnnrfW3gvkfdhz\nrbULQnP+a4z5K05x6zGgQj/doS7Az4FzgEeNMUcvGX4sNPU1a8Nn/fQJvaYV0ZH3HU5BsA+hgiDO\nst5awG5r7foinhsceq7QgqAx5jTgU6ABzjLnW4o6wERERESkzAV8sH6h0wm4ehbkHix83kmDnOXA\nXUdD7cYVm1GkhvEFfNy76F6WbDuy9fnv+/2ea7te62IqkerN6wvwwy7D0p2G+76djy9w7KLH+BgP\nZ3dtxujeLRjSoQmx0Z5C3kmk8oqkIHg6Tofd80eNFyiBW2tTjTG3A+/hLKl9IKKEkbsN+Ay4GTjP\nGPN9aPwUnKLbC8D9+ea3Cb1uLuI9txw1N/+vt3B8hT2X3+nAfTj7MD5jrb3/OPNEREREylYwCFuW\nOJ2AK2dCduHdDzTr6SwH7nYJ1E+u2IwiNZQ/6OcPi//AF1u/CI/d3vt2FUwaCwAAIABJREFUbux+\no4upRKqnQNDy1fpdfLg8gzkrtnMgJyp050gx0GPg9A5NGN27BSO7NSMxrtqe0yo1QCSf3qaA11q7\nNd9YAChso5iPcJbgjqaCC4KhpbqDgDdxlvS2ynf7e2DxUctxE0Ovh4p427wfk+ffCCDS5/IbEXr9\ntjTFQGPMGGBMCaf3BsjKymJmaF8ekeLosyIlpc+KlIY+L5WAtdTL3kSrPV/Tcs83JPgKLwIejGvG\n1gankdFgIAfjm0MW8MUyYFmFxNRnRUqjun1egjbIe4ff4yffT+GxM+LOoNnGZszcVL2+14pW3T4r\nEjlrYesh+D7Lw7Isw35f4Ut9T6pt6dckSN9Glrqxv0D6L3yeXsFhRcpYJAXBgziHieS3D2hw1AEd\nWGv9xpgcoMJ/jBwqBn4A7AdGAV/idDEOBp4F3jfGPGytnZD3SOi1tAeglMXBKV8B/YFTjTEvWmvv\nKuFzrYEzTuDrioiISA2S6N1Gyz1f02rPVyTmZBY6JzumIVsbDCCjwUD2JZzsHPYhIhUqaIPMyJ5R\noBg4OG4wZ8WfpdNGRcpAlheWZhmWZnnIzC7891TjeEv/xpZ+jYMklfKcLJGqIJKCYAbQwxjTwFq7\nJzS2BuegjUHA/LyJxph2OF1x+080aGkYY+oDH+IswR1krd2Q7/ZMY0wa8BPwkDHmv6EThfOOCErk\n+PLu5T9OKNLn8psLPAO8BdxpjLHW2ruLeL88m4AvipsU0huo17hxY53YJ8WaqdMdpYT0WZHS0Oel\nDFgLGUthyYuwdi74vBATDx3OgUF3Qcu+xxbw9qbDivedfQG3pxb+vgkNodvF0OMyEpJPo4PHQ4fy\n/26OS58VKY3q9nmx1jLx64n88POR04Sv7HQl4waMUzHwBFW3z4qUzq6DOcxK/YUPf8hg2Za9hc5p\nnBjLhT1bMLpPSzYt/QJj9HmR4o0bN87tCBGJpCD4HdAD6MmRYtRnwEDgcWPMCmvtdmNMY5zDMSzw\ndVmELYULgCbAgqOKgQBYa9cZY74BhoX+WotTXAM4uYj3zet03JRvLO/XJ5XyuaMzfWCMuQqnKHiX\nMYbiioLW2qnA1KLm5DHGLELdhCIiIlVXwAczboE1n4LfC3lnj/myYdVHToGw0/lw8T/Auw/SZjj7\nAqYf5z/DYhOh84XOvoBth0FUTEV9JyJyHNZanv7uad79+d3w2CUdLuHPA/6sYqBIBA7n+pm3MpMP\nf8ggZW0W/uCxC/tqxUZxTrdmjO7TksHtGhEd5RwOsrlidscQcU0kBcEPgZuA6zhSEPwbcDvQD9hi\njNmJs9egB2d/wUknHrVU8opz+4qYk/cjgYah17wfwXUzxiQc56ThU46aC7AayAYaGmPaHeek4VML\nee4YhRQFrbX2d0U9IyIiIjWAtaFi4CynAHjM/SD4DsOqmbAxBQ7tBILHzouKg44joftl0PEciNEa\nKJHKwlrL88ueZ/qq6eGxC9teyPjTxuMxOr1UaiZrLcvT9/JqygYWrt6J1xcgPiaKMzsncfPQtvRq\nVe+YYrk/EOR/67KYuXwbc9K2czg3cMz7RnsMQzs2YVTvFpzdtSm1YnU4iNQ8kXzq5wIXceSgDKy1\ne4wxZwJv4BTNmodubQXustamnGjQUtoWeu1njIk56vAQjDExOMVLgI0A1tp0Y8wyoC9wOc5hJPmf\nOQPnYJLtOHv+EXou1xgzG7gEuAaYcNRzbXG6J3OBWcUFP6ooeHeoU1BFQRERkZosY6nTGVhYMTC/\ngA8OHbU3oIlyOgB7XAadL4D4euWVUkROwOQfJ/PGijfC1yNPHsnEwROJ8kQV8ZRI9eULBLnnneXM\nX7mDHH+AvOa+bF+A2St+YcHqHZzVNYnnruhNtMewPH0vM5dv45OftpF1MLfQ9+x3cgNG927B+T2a\n0ygxrgK/G5HKp9QFwVBx7ZjClrV2JTDAGJOMUzjbB6yy1p7IYRuRmg0cxukUfN4Yc6+1NgfAGBMH\n/BVnGe8eYE6+554A3gWeMsYssdauCz2TBLwcmvOktfboH7k/CVwM/NEY85m19tvQc4nA6zidki9b\nawvfqOAohRQFrbX296X7WyAiIiLVxpKXnGXCpZF8mlME7DoaEpuUTy4RKRNTUqcw+cfJ4evhycN5\ncuiTRHvUtSQ1k7WWe95ZzryVmXh9x3a8B61TGJyblskFL6aQ4wuweXfhPzRr16Q2F/dpyajeLUlu\nWKu8o4tUGWX+bxhrbTrg6gHc1todxpjbgNdwljJfbIxZinMicD+cDsYc4EZr7b58z71njJkM3Aqk\nGmPmAz5gBFAXZ7n03wr5et8ZYx4AngKWGGMW4CxJPgNIAr4BSrXL5FFFwd+FOgVVFBQREamJ1s45\nsmdgSUQnwE1zip8nIq6bljaNF5a9EL4+veXpPHPGM8R4tK+n1FzL0/cyf+WOQouB+eX4g/ycefCY\n8aQ6cfyql3M4SLcWdbUHp0ghqu2PnKy104wxqcDvgCHAyNCtDJxC4XOhrsajn7vNGPM/nELiGUAU\nzj6BrwOTC+kOzHvuaWPMT8C9OMum44ENwIvAM3kdiqX8Hsq+KLh5Cbzz/+zdeVzUdf7A8dd3hoEB\nVG48wAvv+xa1vNLK2i0ry8rObXVr29rd2p9tu7Udm93Zbm21W7qVtXa42WFbVpoXeR9hIuKFFyi3\ngAIDc3x+f3yQWxgQGGDez8djHjCf73dm3iMIzHven/f7jvNPIhRCCCFEy2OvZ3Wgs57nCyE84sOk\nD3lpx0tl12M7x/K3KX/D1+zrwaiE8LzFcUcodlTv/Veb9n4+zBish4OMiwnDbJLXukLUptESgoZh\nXAXcBfRF98uLB15TSu1srMeoL6XULuD2BtzuA+CDBtzuG/TEZXfPvxO4s45zPgUa7y8C5ao+iVCm\nCgohhBAtm8Vad//AinxkWIgQLd2nBz/lma3PlF0fGTmSV6e+itXH6sGohGgZvk9Kp4aBwOflazax\n/dHpWC3Sc1MId9U5rsowjIGGYewyDOOH0v57NZ3zIno77dXAAGAYOhG3xTCMWxszYNEIzk0iTPpK\nTyz0SJtHIYQQQritxyT3zzVM0HdG08UihLhgXx7+kic2PVF2fWjEUN6Y/gYBFulvJrxXer6NZdtP\n8Julu+rcKlyV3eWSZKAQ9eROheB0YDiwpKZtr4ZhXIHeJgt6kMcPQAFwCRAEvGkYxgal1PHGCVk0\nGkeRnliYuguiR9V9vhBCCCGa35k0SN/j/vk+Vhh/X9PFI4S4IN8e/ZZHNz6KQr8pPyB0AP+c/k8C\nLYEejkyI5lXicLHz2GnWH8hk3f4MktLONPi+rD6SDBSivtxJCE4GFPDJeY7PL/2YDUxQSh0EMAyj\nIxAH9ALmAo9dWKiiSThssPk1uOEdT0cihBBCiKoKsuC9mZB/0r3zffx1S5CokU0blxCiQdYcX8PD\nGx7GVdqWvE9IH9669C06+HbwcGRCNI/U3CLW79cJwE2Hszlb7Ljg+zQZMG1AZCNEJ4R3cSch2Lf0\n48aqBwzDCAImoROGC84lAwGUUumGYTwJvI+e0isJwZZIueCA220PhRBCCNFcinLh/WshM6l0wQTd\nYuHUbv2GXsU5Z4ZJVwae6w8sQ8OEaHHiUuL4w/o/4FA6ARITFMOiSxcRbA32cGRCNJ1ih5PtR06z\nbn8G6w9kcjCj+kTgcyxmgzE9QpnSL4KO7a08/Okeiux1Dxbx8zEzd2JMY4YthFdwJyHYCTijlMqt\n4dg4dB9CBSyv4fiK0o99GhaeaBaOejQpF0IIIUTTKz4DS6+HtJ9KFwyYtQgGz9KtPja9qgeEOYp0\nVWDfGTDhPoiSFiBCtERbTm3h92t/j8Olk4Hd2ndj8WWLCfMP83BkQjS+Y9kFrD+Qyfr9mWw6nF1r\nUi86xJ8p/SKY3DeSCb3CCPTTKQqlFKuT0lmVmF5rP0GrxcT0gZEMiw5q9OchRFvnTkKwA1Ctd2Cp\nMaUfjymlUqoeVEqdMQzjDLqXoGipZBKhEEII0XLYi+DDmyFle/nazNdgyPX68+hRMHuJZ2ITQtTb\njrQd3P/9/ZS4SgCIahfFvy//NxEBER6OTIjGUVTiZMuRbNbvz2T9gUyOZBWc91xfHxPjYsKY3DeC\nKf0iiAkPxKihqt0wDF6ePZwHl8WzOjGDYoez0tRhk6ErA6cPjOTl2cNrvA8hRO3cSQjmAWGGYQTX\nUCUYW/pxZy23N4C663yFhxjQ93JPByGEEEIIAEcxfHwrHI0rX7vyJRhxq+diEkI02O7M3fzm+99g\nc9oA6BjQkcWXLaZTYCcPRyZEwymlSM4qYF1pAnBrcjbFjvNX8fUMD2Ry3wgm94tgXM8w/H3dGwBi\nMZt49aYR7E7JY9GGZNYkZWBzOLH6mJk2IJJ5E2MY1lW23AvRUO4kBBPQg0VuABadWzQMox0wBb1d\nOK6mGxqGEQK0A45eYJyiyShwloDTDmaLp4MRQgghvJfTDp/cBYdWl69d+lcYO89zMQkhGmxv9l5+\nverXFDoKAQj3D+ffl/+b6PbRHo5MiPorKHaw6XA26w9ksG5/Jimnz992yt9iZkKvMCb3i2By3wi6\nhzV8grZhGAzvGszrt8iwLCEamzsJwc/Rib8nDMPYoZT60TAMX+BVIBBd/ffpeW57UenHxAsNVDSh\npK900/LZ70FAqKejEUIIIbyPywmf3QNJ/ytfm/wwXPQ7z8UkhGiw/Tn7uXvV3ZyxnwEg1BrK4ssW\n071Ddw9HJoR7lFIcSD9blgDcfjQHu1Od9/w+ke1KtwFHMrpHCFaLe1WAQgjPcSchuAj4LRAD7DAM\nIx0IBSzo6sD/1NQ/sNTs0nN+aIRYRWMxTODjBwHhkHdCrx2Ng8XTYM4yCJcZMEIIIUSzcbngy99B\nwiflaxPuhykPey4mIUSDJecm86tVvyKvOA+AIL8g3rr0LXoF9/JwZELULt9mZ9OhrLKtwKfybOc9\nN9DXzEW9w5nSL5JJfcOJDgloxkiFEI2hzoSgUqrIMIwZ6ErBgeipw+esB+6v6XaGYXQESrtfs/IC\n4xSNxTDBwGv0JMIuIyHuJVizQB/LSYZF02D2u9DrEo+GKYQQQngFpeCbh+HH98vXxsyFS58CaZAu\nRKtzLP8Yc7+bS44tB4B2lna8eemb9Avt5+HIRFullCL+RC6L4pJZm5SJze7EajFzSf9I5k2KYVh0\n0HkHbiil2Hsyv2wi8M7jp3G6zl8FOKBzh7JhICO7heDrY2qqpyWEaAbuVAiilDpkGMYwYDowpHR5\nh1JqXS03CwfmA3al1O4LilI0nu4T4IZ3yq9Pmg/hfeHTu8FRBMV58J/r4YrnpWeREEII0ZSUgtVP\nwLY3y9eG3wJXvCjJQCFaoZQzKfzy21+SWZQJQIBPAP+69F8MChvk4chEW2V3umqcwltkd7Iy4RRr\nkjLKpvBazDp5l1tYQtxBXQW44WAmmWeKz3v/Haw+TOwTUdYLsGMHa3M8LSFEM3ErIQiglHIC35Ze\n3Dl/L7C3gXGJ5jRwJgR3hw9vhjMnQTnh6/+DzCSY8TyY3f42EUIIIYS7NrwIG/9efn3QdXD1P8Ak\nFRdCtDZpBWnM/W4u6YXpAPj7+PPG9DcYFjHMw5GJtkopxYPL4lmVmI7NXn3Cr0vpxOCqxHTuenc7\no7qFsOFgJvEncqmlCJAhUUFMKU0ADu8ajI9ZficJ0VZJpkdoXYbDvDXw0Rw4uUuvbV8M2YfghnfB\nP8Sj4QkhhBBtyqbXYO3T5df7XQnXvQUmacIuRGuTWZjJ3O/mkno2FQBfky+vXvIqozqO8nBkoi2L\nP5HL6sSMGpOBFdnsLuIOZhF3MKvG4yEBFib11QnASX0jCG/n1xThCiFaIEkIinIdOsMvvobP74W9\npYOjk9fB4kthzscQJo2QhRBCiAu2/d/w3SPl12OmwvXvgNniuZiEELVSSrEnaw/v7n2XuJQ4ip3F\n+Jn9GNd5HAdOH+BkwUkAfEw+/H3q3xnXeZyHIxZt3eK4IxQ7nPW+nWHA8K7BTOkbyeR+EQyJCsJs\nkjYVQngjSQiKyiz+cP3bENEP1j2r17IPwqJLYPZ7EDPZs/EJIYQQrVn8h/DVg+XXu02Amz4Ai/Rl\nEqKlsrvsPBL3CGtPrKXEWYILXZFlc9pYl7Ku7DwzZhZOXsjE6IkeilR4i3ybnVWJ6bVu/a3KbDL4\n243Dmdg7nJBA36YLTgjRakhCUFRnGDDlYQjvo6sFHTaw5cJ/roMrX4TRd3k6QiGEEKL12fsZfHFv\n+fWoUboC3zfAczEJIWqllCpLBtqctlrPHRIxhKldpzZTZMKbnC4oYdvRHLYm57D1SDb7TuXXKxkI\n4FKKq4d1aZoAhRCtkiQExfkNngUhPeDDOXA2DVwO+N8DkHkALlsgw0aEEEIId+1fCcvngirt9dRx\nCNy6HKwdPBuXEKJWe7L2sC5lXZ3JQID9p/eTkJXAkIghzRCZaMsyzxSz7YhO/m1NzmF/+pkLvk+r\nj/SoFUJUJhkdUbuoUfCrtfDhTXBqt17b+k+9jfj6t8Ea5Nn4hBBCiJbu8FpYdrt+Yw0gvC/c9pkM\n7BKiFViydwnFjmK3zi12FLMkcQkvTX6piaMSbc2pvCK2HclhS2kFYHJmQa3nmwxo5+dDvs3h1v2b\nDJg2ILIxQhVCtCGSEBR169AFfrESPrsH9q3Qa4dWlw4b+QhCYzwbnxBCCNFSHdsMH80BZ4m+HtID\nbv8C2kV4NCwhhHs2pGwo6xlYFxcuNqRsaOKIRGunlCLldBFbj+SwNTmbrUdyOJ5TWOttfEwGQ6KD\niO0ZRmxMKKO7h3Ao4yxzFm2lyF73YBE/HzNzJ8prNiFEZZIQFO7xDYQblsC6Z2DDi3ota78eNnLj\nf6DHxZ6NTwghhGhpUnfC0hvAXvpCr0M03L5Cv9EmhGgVip3uVQeeY3PUvbVYeBelFEeyCkq3AOsk\n4Mm82r9PfM0mhncNJjYmlNieYYzsHkyAb+WX7sO7BjN9YCSrEtOx2c+ftLZaTEwfGMmwaNnZJYSo\nTBKCwn0mE1zyKIT3gy9+A85iKDoN710DP38ZRt7u6QiFEEKIliEtAd6/DkpK+z616wh3rICQ7p6N\nSwjhNqUUPiYf7C6727ex+sjEcG+nlOJgxtmy5N+2IzlknKk9sWy1mBjVPYSxPXQF4PCuwVgttff8\nMwyDl2cP58Fl8axOzKDY4aw0aMRk6MrA6QMjeXn2cAzDaIynJ4RoQy4oIWgYhg/QGwgBLLWdq5SS\n+vm2YugNENoTPrwZCjLAZYcV90Pmfrj0r2CShrVCCCG8WOYBeG8m2HL1df9QvU04rJdn4xJCuC31\nbCpPbXmqXslAEyYmRU9qwqhES+RyKfal5esKwOQcth3NIaegpNbbBPqaGd0jlLE9QxkXE8qQqGB8\nfUz1fmyL2cSrN41gd0oeizYksyYpA5vDidXHzLQBkcybGMOwrsENfWpCiDauQQlBwzB6As8CVwN+\nbtxENfSxRAsVPRrmrdFJwfQ9em3za5B1EGYtlqmJQgghvFPOEXjvaijM0tf9gvQAkcgBno1LCOEW\nh8vB0n1LeT3+dYocRfW6ra/ZlzsG3tFEkYmWwuF0sfdkPluP6Oq/bUdy6hzu0cHqw9ieOgEY2zOM\nQV064GOufwKwJoZhMLxrMK/fMrJR7k8I4T3qnaQzDKM3sBkIBQx0si8DkIYZ3ia4K9z1DXx2NyT9\nT68d/Bb+fZkeNhLSw6PhCSGEEM0qL0UnA8+c0tctgXDrJ9BluGfjEkK4JTE7kSc2PcG+nH2V1rt3\n6E5aQVqt/QStZitTu05lcPjgpg5T1JNSivgTuSyKS2bVXjN2Fzy84xsu6R/JvEkxDIsOqnU7bYnD\nxZ7UXLYk6+TfzmOnOVtcewIwNNCXsaUVgLExofTv1AGzSbbsCiFaloZU7T0FhAEpwO+BFUop9+ad\ni7bHrx3Mfh/WPAU/vKzXMveVDhtZCt3HezY+IYQQojmczdDbhHOP6+s+Vv3mWNexno1LCFGnQnsh\nb8S/wfv73selyocz9AnpwxPjn2BA2AAeiXuEdSnrKHYUV5o6bMKEr9mXqV2n8vTEp6VPWwtjd7qq\n9NjTX58iu5OVCadYk5RR1mPPUlqxZ7M7iT+RW7r9N5udx07XOrQDIKK9H7E9Q4mNCSO2Zyi9I9ph\nkgSgEKKFa0hC8BJ0VeDNSqmNjRyPaI1MJpj+OET0070EnSVQmK2rJK56BYbP8XSEQgghRNMpzNHJ\nwOxD+rrJAjf+B3pKLzEhWrq4lDgWbFnAyYKTZWt+Zj/uGXYPdwy6A4tJt0l/ftLzJGQl8O7ed4lL\njcPmsGH1sTIpehJ3DrpTKgNbIKUUDy6LP+8UXpfSicFVe9O57d/bGN09mG1HTxN/IpcSR+0JwC5B\n1rLk39ieofQMD5RksBCi1WlIQrA9UCTJQFHNsJv0NuGPbtG9k5wl8PmvITMJpj0uw0aEEEK0PbY8\neP9ayEjU1w0z3PAO9LnUs3EJIWqVVZTFC9tfYOWRlZXWYzvH8ti4x+jWoVuldcMwGBIxhIVTFjZn\nmOICxJ/IZXViRp3VfTaHiy3J2WxJzj7vOd1CAypVAEaH+EsCUAjR6jUkIXgc6GYYhqGUUnWeLbxL\nt3Hlw0Yy9uq1ja/oYSPXLdJbjIUQQoi2oPgsLL0BTsWXLhhw7Zsw4CqPhiWEOD+lFJ8f+pyXdrxE\nfkl+2XqwXzDzx8znqpirJNHTRiyOO0Kxw9mg28ZEBBLbM4xxMboCsHOQfyNHJ4QQnteQhOBHwF+A\nacDqxg1HtAkh3eGX38LyuXDgG722/2t4+3K4+SM9jEQIIYRozexF8NHNcGJr+dpVr8DQGzwXkxCi\nVkfzjvLXLX9le9r2SutXxVzF/435P0KtoR6KTDSFNUkZuOpRvmI2Gbx60wjG9gwlor1f0wUmhBAt\nRENmnT8H7AbeNAyjZyPHI9oKv/Zw0wcw4bfla+kJsGgqnNjmubiEEEKIC+UogWW3w5EN5WsznodR\nd3guJiHEedmddt766S1mrZhVKRkY1S6KN6e/yTMTn5FkYBtyOPMsL3yTRJG9ftWBLqX42dDOkgwU\nQniNhlQIzgbeAZ4E9hiG8QmwHThT242UUu814LFEa2Yyw2VPQXhf+N8D4LJDQSa8+3OY+RoMne3p\nCIUQQoj6cTpg+S/h4Hfla9Meg3H3eC4mIcR5xWfE8+TmJzmUe6hszWyYuX3Q7fx62K/x95GtoG1B\nbmEJX+4+ySe7Utl9IrdB92H1kX7nQgjv0pCE4LvoKcPnmmvcVnqpiyQEvdXI2yA0Bj6+FYpywFkM\nn87Tw0amPqqnFAshhBAtncsFX9wL+1aUr02aDxP/4LmYhBA1OlNyhld2vcKy/ctQlO8bHRQ2iCcm\nPEH/0P4ejE40BrvTxfr9mSzflcL3+zIocdY+PKQ2JgOmDYhsxOiEEKLla0hCcAMgw0RE/fS4qHTY\nyE06EQgQtxCyDugG7L6Bno1PCCGEqI1S8NUD8NPH5WvjfgNTH/FcTEKIGn1//Hue2fIMGUUZZWv+\nPv7cP+J+5vSfg9kklWCtlVKKvSfzWb4rhRXxJ8kuKKl2jsVsMK1/R0Z0C+bvqw+6tXXYz8fM3Ikx\nTRGyEEK0WPVOCCqlpjRBHMIbhPaEX66CT+6CQ6v02r4v4fQxPWwkKMqz8QkhhBA1UQq+/TPsfLd8\nbdQv4PKnQaaRCtFipBek8+y2Z/n++PeV1idFT+KR2Efo0q6LhyITFyrjjI0vfjzJ8l0pJKXV3Klq\nWNdgrh8Zxc+HdiEk0BelFAkn81iVmI7Nfv7qQavFxPSBkQyLDmqq8IUQokVqSIWgEA1n7QBzPobv\n/gJbXtdraT/pYSM3fQjRozwbnxBCCFHVmgWw5Y3y60Nvgp+9LMlAIVoIl3KxvWQ7z37xLAX2grL1\nMGsYD8c+zOXdL8eQ/6+tjs3uZFViOst3pbDhQGaNE4M7dbBy7cgoZo2Mondk+0rHDMPg5dnDeXBZ\nPKsTMyh2OCvdh8nQlYHTB0by8uzh8j0ihPA6khAUzc9khhnPQERf+OoP4HLA2XR490qY+ToMud7T\nEQohhBDahpcg7qXy6wNn6t9V0v9WiBbh0OlDLDq7iBPOE5XWZ/WZxQOjHiDIT6q+WhOlFLuOn+aT\nnan876eTnLE5qp1jtZi4YnBnZo2MZnyvMMym8yfyLGYTr940gt0peSzakMyqvSexu8Bq8WHagEjm\nTYxhWNfgpnxKQgjRYklCUHjOqDshtBcsuw2KToPDpic3Zh2AyQ/Liy0hhBCetfkNWPNU+fU+l8N1\ni8Esfz4J4WnFzmLe+ukt3k54G4erPGnUo0MPHh//OKM7jfZgdKK+Uk4X8umuVD7dlcLR7MIaz4nt\nGcqsUdFcOaQz7fzc/zlsGAbDuwbz+i0j+eILnTieOfPnjRK3EEK0Zg3+i9YwjDHAPcBFQBegtqkQ\nSiklfz2L6npOhLnf62EjWQf02vrnIXM/XPNP8A3wbHxCCCG808534ds/lV/vORlmvwc+vh4LSQih\nbU/bzpObn+RY/rGyNTNm5g2bx9whc/Ez+3kwOuGus8UOVu45xfJdKWxJzqnxnO5hAVw3IprrRkbR\nNVReFwghRGNqUJLOMIyHgQWAuyVc0pBBnF9Yr9JhI7+Aw2v0WuLncPoo3PwhdJAG0G2SUpC6Eza9\nCge/A7sNLFZdgTPhtxA1UvpzCSE8Y/fH8OXvy693Had/H1msnotJCEFecR4Ldyzks0OfVVrvZu7G\nNQHXMG/4PA9FJtzldCk2H87m010prExIq3ECcHs/H34+TG8JHtU9RHr7CSFEE6l3QtAwjKnAM4AT\neAz4H7ALyATGAx2B6cD9pTf5JfBTYwQr2jD/YJjzXz3Fcdubeu1UPCy6RL8I6zLCs/GJxuW0w2f3\nwP6v9VZxVTr5zV4E+1boBGG/K+Haf4HZ4tlYhRDeJXEFfP5roLTtz8L0AAAgAElEQVTzfJcRcMsy\n8K1tI4QQoikppVh5ZCXPb3+eHFt5JVk7SzseGPUAln0WTIa0mmnJDmeeZfnOFD77MZVTebZqx00G\nTOwTwaxR0Vw2sCNWi9kDUQohhHdpSIXg/ei/kh9XSj0DnHvXxqmUSgaSgc2GYSwG1gH/BiSbI+pm\n9oErX9DDRr5+CJQTzpyCt6/QiaFB13g6QtEYlCpNBn6lE4DVjrvAXghJX+nzZi2WSkEhRPM48B18\ncpf+/QMQORBu/RSsMpRACE9JPZvKgi0L+CH1h0rrl3a/lIfHPkxkQCRfJH3hoehEbXILS/hy90k+\n2ZXK7hO5NZ7Tr2N7Zo2K4prhUUR2kCpsIYRoTg1JCMaWfnyrynqlt+WUUqcMw7gXWAX8GfhNAx5L\neKMxc/Wwkf/eAbY8cBTpz7MegUnzJTnU2qXu1JWBNSUDK3IU6fNSd0H0qOaJTQjhvZLXw8e3gsuu\nr4f1htu/gIBQz8YlhJdyuBws3beU1+Nfp8hR/jdDZEAkj8Y+ytRuUz0YnTgfu9PF+v2ZLN+Vwvf7\nMihxuqqdExroy9XDunD9qGgGdekgW4KFEMJDGpIQDAcKlFJZFdYcQE1dXtcARcAVDXgc4c16TYW5\na+CD2ZBzWK+tfRoyk2Dm62Dx92x8ouE2/UNvE3aHwwabX4Mb3mnamIQQ3u34VvjwZnAW6+vB3eH2\nFdAu0rNxCeGlErMTeWLTE+zL2Ve2ZmBwc/+buX/E/bTzbefB6ERVSin2nsxn+a4UVsSfJLugpNo5\nFrPBtP4dmTUqmsl9I/D1kS3eQgjhaQ1JCJ4GOtSwFm4YRpBSKu/colJKGYbhAjpfQIzCW4X3hnnf\nw7I74Mh6vZawXA8buekDaN/Jo+GJBjr4bXnPwLooFxz4pmnjEUJ4t5M/wtLrwV6gr7fvAnesgKAo\nz8YlhBcqtBfyRvwbvL/vfVwV/lboE9KHJ8Y/wdCIoR6Mrm1TShF/IpdFccmsTcrEZnditZi5pH8k\n8ybFMCw6qFolX8YZG1/8eJLlu1JISjtT4/0Oiw5i1qhorhrahZBAmdIuhBAtSUMSginACMMwIpRS\nmaVricAkYApQ1sTDMIxhQCBQ8xx5IeriHwK3LoeVD8GOt/Va6k49bOSmD8DlkCm1rY3dzerAcxx1\nbC0WQoiGSk+E96+F4nx9PTBCJwNDeng0LCG80Q+pP/DU5qc4WXCybM3P7Mc9w+7hjkF3YDHJkLGm\nYne6eHBZPKsTMyh2OHGVzlQqsjtZmXCKNUkZTB8Yycuzh+N0KVYlpvPprhQ2HMzCee7kCjp1sHLt\nyChmjYyid2T7Zn42Qggh3NWQhOBG9JCQ0cDK0rUVwGTgJcMwTgLxwBDgbfQAkvUXHqrwWmYL/Oxl\niBgA3/xRV43lp+qkoMmsk4IypbblcxSXJnWr/+FYK5MFHCXgI+8qCyEaUdYheG8mFJ3W163BcNvn\nEN7Hs3EJ4WWyi7J5YfsLfH3k60rrsZ1jeWzcY3Tr0M1DkXkHpRQPLotnVWI6Nnv1HRwupROD3+5N\nZ9rC9eQUFHO22FntPKvFxIxBnZg1KpoJvcIxm+RNeSGEaOkakhD8DD1p+A7KE4L/BO4B+gBbKpxr\nAIXAEw0PUQh0pV/sryCsFyy7E0ry9RRIZ/U/SGRKbQvjcsLuj2Dds5B3ov63d5bAP0bCxb+HEbeB\nj1/jxyiE8C6nj8F7V0NBhr7u2x5u+xQ6DfZsXKJNU0qxJ2sP7+59l7iUOIqdxfiZ/ZgUPYk7B93J\n4PDBXjVcQSnF54c+56UdL5Ffkl+2HuQXxPzR87m619Ve9e/hKfEnclmdmFFjMrCiEoeL4zmF1dZj\ne4Yya1Q0VwzuRHurvAkvhBCtSUMSghvQ1X9l3WKVUjbDMCYDrwBXA37oMqDNwANKqT2NEKsQ0Hsa\nXPV3WP5L6qw0kym1nqUUJP0P1izQw2AqMahXpWDeCfjqD7BhIVz0Oxh1hwyWEUI0TP5JWHKVrjQH\nsATALf+FKPk9IZqO3WXnkbhHWHtiLSXOElzo5IvNaWP1sdXEpcYxJXoKT0982iu2xh7NO8pTW55i\nW9q2Sus/j/k588fMJ9Qq072by+K4IxQ7aniDvRbdwwK4bkQ0142MomtoTXMlhRBCtAb1TggqpVzA\n3hrW04AbDcOwoCcR5yulCi48RCGq2LdCV/wpNxJKMqXWM5LXw/dP6n6PFQWEw8Q/QMo22P9N7f0B\nfawQ1lu/eC8qbUN65qTeNh63ECbcD6PvAj+ZNCiEcNPZTL1NOPeYvm72g5s/hO7jPRuXaNOUUmXJ\nQJuzeh9dFy6KHEWsPbGWR+Ie4flJz7fKyjh3KiAdLgfv7H2HN3e/SYmrfBJtVLsoHhv3GBOiJnjw\nGXinNUkZ1NAG8Lz8fEys+78prfJ7VAghRGUNqRCslVLKDpxq7PsVooxMqW25UnfB93+F5LWV133b\n6wTe+HvBrz045+nt3Pu/1knbil9Pw6STged6QDpLYMc7sPGV8u19BRmw6i/ww99g/G9g7K/AWnX4\nuRDCKyml34yoOnAqZqquVs5J1ueZfODG9yFmiiejFV5gT9Ye1qWsqzEZWJHNaWNdyjoSshIYEjGk\nmaJrHO5UQA6LGEZmYSaH8w6X3c5smLl94O3cM+weAixSadbcbHYnRfb6VQeWOF2SDBRCiDai0ROC\nQjS5+k6ptcuU2iaXeQDWLoDELyqvm/1g7Dy4+EEIDKuwbtG9HVN3lb9odxSBjz/0nQET7ivfvme2\n6Otjfgm73oMf/q4rBUFXDq55St/HuHsh9m49mVoI4Z2c9prfbLCXtpAoY+ifQX0v90iYwrss2buE\nYkexW+faHDbeSXiHhVMWtpqki7sVkFtObam0PjBsIE+Mf4IBYQOaK1RR6lDGWT7cdpxPdqbU+7ZW\nH3MTRCSEEMITak0IGoZxbqyXXSl1qspavSiljjfkdkJUY7HWM8mn4L936iqybuNlwEhjykuBdc9B\n/NLqVX7Db4EpD0NQdM23NQzd23H2Evcey+KvE36j7tSPF/c3yCv9sWLL00NLNr+uE5DjflM5ASmE\naPuUKk0GflX374ioUTDwmuaJS3i9DSkbyirm6qJQrDq+imHvDcPqY8Xfxx9/H3+sZmvZ9fquVzxW\ncd1isjRK0tHdCshz/Mx+/G7k75jTfw5mkySXmkuJw8W3e9NYuvUYW5JzGnQfJgOmDYhs5MiEEEJ4\nSl0VgkdKPyYBg6qs1Ydy47GEcE+fy3UfQXe3DQPs/UxfOg7RCaMhN4CvbE1psIJs+OFl2LYInFWq\nHgbOhKmPQkTfpnlsHz/dO3DEbXp6cdxCOF36Y6k4X1/f8i9dUTjhfmgnf7gK4RVSd+oqQHfeMMpI\nlIFTotkUV/096QaFoshRRFFtvXYvkNkwY/WxYjXXkFB0Y/3c9SV7l2BzuL97I7ZTLLcNvK3Jnpeo\n7Hh2IR9sO85/d5wgu6Ck2vHIDn6cLijB7qy7kaCfj5m5E2OaIkwhhBAeUFeS7tzbhqYa1upDSrJE\n45lwf2lfqMK6zzVMlROH6Xvgy9/Cqsdg5G0wZi6E9GiyUNuc4jOw+Q3Y9A8oOVP5WMxUmPYYRI1s\nnljMFv01HHYzJCyHuJcg64A+Zi/Q24i3LdIVhRf9Fjp0aZ64hBCesekfepuwO2TglGhGfmY/t6vn\nmpNTOSmwF1Bgb94ZgNvTtzfr43kjh9PF6n0ZLN16jLiDWdWOm00G0wdEMie2Oxf3CuP3y+JZlZiO\nzX7+N9utFhPTB0YyLDqoKUMXQgjRjGpNCCqlTO6sCdGsokbpgRNJX9UxpdYf+v8MLvo9bF8EPy0r\nP9+Wq188bnoN+l2hqwZjpsp24vNxFMOOt2HDS1BY5Q/LqFEw7XGImeyZ2Mw+MOxGGHK97mG44UVd\n/QP66731n7Dj37qi8OIHILirZ+IUQjQtGTglWiCHy0GXdl1Izkt263wTJi7tcSnPTXyOYmdxWZWg\nzWHD5rDpz522GteLnEXl59SwXvFYkbMIh8vRxM++ZvWpJhT1czK3iI+2n+Dj7cdJz69emdo5yMpN\nY7px45iudAqylq2/PHs4Dy6LZ3ViBsUOZ6WpwyZDVwZOHxjJy7OHt5relkIIIeom23hF62MYevqs\nu1NqzRa4+lW49En48T+6aiz3WOnJSt/H/q8hrI/uMzj8Zj0JV4DLqbflrnsW8k5UPhbeD6b9Bfr/\nvGUkUk1mGHyd7gu2/ytY/wKk/aSPOUt0UnDXe/rre/GDENrTs/G2ZhWmuP5s30rMqgQS/PV2/gm/\n1VWiLeF7QniX+g6casKtmEIAnDp7ij/G/dHtZCCAr9mXOwbegY/JBx+TD4GWwCaLz+6yV04cVkg2\n1rRe6XqF5OQPKT+43SMRwOpjrfsk4TanS7H+QAYfbD3OmqSMSsk80L+Op/SN4JbY7kzpF4GPuXpt\nh8Vs4tWbRrA7JY9FG5JZk5SBzeHE6mNm2oBI5k2MYVjX4GZ6RkIIIZqLJARF61SfKbXn+Ifo7cbj\n7oWDq2Dbm3B4Tfnx7IOwcj58/1edNBr7Kwjv07zPq6VQis55O+GNpyFrf+VjQV1hyp9g2E06CdfS\nmEww4CqdqDz4nU4Mpu7Qx1x2nRT8cSkMnQ0T/+C9X+OGqjLF1afiFNd9K/S/ecVkvBBNLfuwHihE\n3f2vKvHxb5JwhAD4/vj3PLbxMfJL8svWDAxULd+nVrOVqV2nMjh8cHOEiMVkweJrob3vhb0J+od1\nf2D1sdVuJQVNmJgUPemCHk9oGfk2lu04wYfbTpCaW/0NjvB2ftw0pis3julK19C6+2YbhsHwrsG8\nfksztX4RQgjhcZIQFK1XfafUnmMyQ78Z+pJ1UFcMxn9Q3hOv5Axse0tfYqbqybZ9LmuZya+mkLye\nSQeeJKSwSkVDQDhMmg+jf6EHe7R0hgF9L9dfu+S1OjF4fLM+ppyw+0P46WMYdK1+XpEDPBtva1DX\nFFfl0r09k77S581aLJWCoumk7IRNr8C+L+s3ZAp0JXnfGU0Tl/Bqxc5iXt7xMh8kfVC2ZjbM/Hr4\nrzl0+hDrU9ZT7CiulDwzYcLX7MvUrlN5euLTrW5L5h2D7iAuNc6tASjnKiBFw7hcik2Hs1m69Rir\nEtNxVC0HBC7uHc6c2G5cOrAjlhqqAYUQQohzak0IGobxdiM9jlJK/bKR7kuIxhPeB658QW993f2R\nTgKeG0wBOpGUvBaCu+sBJCNuhYBQz8XblFJ3wfdPQvI6Qiqu+7bXlZXj722dW6kNA3pdopO7R3+A\nDS/AkQ36mHLpgSQJy2HA1Tox2HmoZ+Ntydyd4uoo0ufJFFfR2FwuOLQKNr4Kx36ofrzqIKnz8bHC\n+PsaPz7h1Y7mHWX+hvkk5SSVrXUO7MwLk15geORwlFIkZCXw7t53iUuNw+awYfWxMil6EncOurPZ\nKgMb25DwIUyJnsLaE2trHZ7S3BWQbUn22WI+2ZnCB9uOcyy7+lC9kAALN4zuys1ju9EzvOm2mQsh\nhGhb6qoQvBO9B6emtyorviVV9XjVYwqQhKBoufza68EiY+ZC8jpdNbj/a8q+lXOPwaq/wNpnYOgN\nMPZu6NRG/qDNPABrntLbPStwGhbM4+7W/fYCwzwUXCMyDOg5UV+Ob9WJwUOry4/vW6Evfa+AyfOr\nbzkXMsVVeI6jBPb8V38PZu6rfrz3dBh/P/z4HiR9XffAqX5XNt9EdOEVVhxewYItCypVyU3rNo0n\nJzxJkJ+eymoYBkMihrBwykJPhdkkDMPg6YlP80jcI6xLWdfmKiA9RSnFtiM5LN16nG8S0ihxVn+z\nY2yPUG4Z143LB3XCavGSnSxCCCEaTV0JwSfPs+4L3AsEAceADUAqOvnXGZgE9ABygX8B1cdcCdES\nGQb0mqovp4/B9sW655wtVx93FOnru96DbhMg9le6V11r7JWWewLWP6e3S1cZynIsdCJJna7h8svv\n8lx8TalbLNy6XG853PAiHFhZfuzASn3pPR0mPaTP9Xb2IsjYp7cKyxRX0ZxsebDzXdjyLzhzsvIx\nkw8Mvl5XMJ97g6bHRe4PnJKkhGgEhfZCFmxZwJfJX5at+Zp8mT9mPjf2u9Frkl8Wk4XnJz3fJisg\nm1teoZ1Pf0xh6dbjHMo4W+14e6sPs0ZGc0tsN/p0bIU7N4QQQrQYtSYElVLVEoKGYfgCa0tve5tS\namlNtzUM42bgLWAiMO3CQxWimYV0h8ue0gM0Ej6BrW9B+p7y48c36Uv7LjD6Lhh1J7SL8Fi4bivI\nhriFOtnprJKrHzgTpj5K/KYaKnDaouhRMOcjOPWTTgxWrJI8tFpfek7SicEeF7f9BIJSkH8S0vfq\n7/W0BEhPgOxD9e/RBrqf4NY3dXI1rFfjxyvarvyTsOWfOhlYnF/5mG87/fN23K8hKLrysYYMnBKi\ngfZl72P+hvkcyz9WttajQw9enPwi/UP7ezAyz2irFZDNQSlF/Ilclm49zpe7T1LsqP47d1jXYG6J\n7cZVQ7vg7yvVgEIIIS5cQ4aKPAyMA+44XzIQQCn1oWEYZuA94CFgQcNCFMLDfANg5O0w4jY4vkX3\nGdy3AlwOffzMSVi7QG9BHXSt3k7cEvumFZ/Rkzg3vVY+QOWcmKkw7bEKW+i8JCF4TuehcOP7kJ4I\ncS9BwqeUbRc/skFfuo2HyQ/pf6u2kBi02yAzSSf8ziX+0hOg6HTjPs7Kh/TH0F7Q51J96X4xWKyN\n+ziibcjYp7cF/7RMTwWvqF1HiL1HvwHjH3z++2jowCkh3KSU4oOkD1i4YyH2Ct+nM3vN5M+xfybA\nUvdEVyEAzhY7+PzHVJZuPc6+U/nVjgf6mpk5Ioo5Y7sxOCrIAxEKIYRoyxqSELwZKAE+dOPcj4BF\nwBwkIShaO8OA7uP1Jf8k7HgHdr4DBZn6uLNET6396WPoMlJPJx50recn8tptsONtnegqzK58LGoU\nTHscYiZ7JraWpuNAuP5tXRUat1AnJZRTHzu+Gd6/FqJG68Rgn8taR2JQKTiTVp7wO5f8yzpY/tzq\nZEBojN46fOYUldvEuinnMGw9DFv/pau1ek7U/4a9p0Noz/rfn2g7lIJjG/WgkIPfVj8e3ldvCx56\no+d/ngqvl1ecx182/oW1J9aWrQX4BPDouEe5qtdVHoxMtCYJqXks3XqcFfGpFJRU/108oHMHbh3X\njZnDo2jn15CXa0IIIUTdGvIbpjtgU6ruV5JKKYdhGLbS2wjRdnToApc8ApP+DxK/0FWDKdvLj5/c\nBZ/dDd8+ore2jb4LgqKaN0anA376CNY9B3knKh+L6A+X/AX6/6x1JLWaW3gf3WNs8kPww990n8Vz\nFaGpO+CD2dB5mJ5K3O9nYDJ5Nt5zHMWQub9C4m+P3v5bNRFcG78O0HFQ6WUwdBoCkQPANxBSdsCS\nq/RW4Lr4WHVSPHO/rrCseBtHkd7KefA7fT2sd3lysPtFUj3oLVxO2Pel3tqburP68W7jYcJv9Vbf\nlvJ/THi1Xem7+GPcH0krSCtbGxA6gBcnv0j3DvKnrqhdUYmTL386ydKtx9l9IrfacavFxFVDuzAn\nthvDuwZ7Tf9JIYQQntOQhOAZINwwjMFKqYTaTjQMYwh68EhGQ4ITosXz8YOhs/UldZdODCYs19WC\nAIVZujLvh7/BgJ/r7cTdJzRtEk4p/SJ7zQLI2l/5WFBXmPpnXWljkv4zdQqNgav/oRN/P/wdfny/\n/Gt7ajd8fCtEDtKJ4YEzK/+bKqWTHOf6mNltOtHV53Kd5Iga2fDvA6XgbEaFPn97S6v+DpQnLt19\nfh0HQccheihDx0EQ3P38cUWN0gMZkr6qe4pr/5/B9Cf1fdltut/mwVX6kn2w8vnZh/RlyxtgCdB9\nG3tP19uLQ3q4/3xEq2Bylegeppteg9NHqhw19PfORb+DrmM9Ep8QVTldThbvWcwbu9/AVaGf6q0D\nbuWBUQ/ga/b1YHSipTuQfoYPth5n+a4Uztiq/47uHdmOW2K7cd2IaIICWuGQOiGEEK1WQxKCa4Ab\ngbcNw7hcKVVjwynDMIKBf6P3lq1peIhCtBJRI3VV2WULdCP8HW9Dfqo+ppy6kjDxC111NXYeDJmt\n+xM2puR18P1fq1fbBITrpNboX8iWu4YI7gY/f1kn/ja+qreKO2z6WMZe+OQXelvjxP+DwbMAVfOk\nU3uR7j958LvySad1Tah2lOjEbsU+f2kJOtnsLt92FSr+BuuPkQPBr139/h0MQ8dc3ymuFiv0ukRf\nZjwLOUf0wJaDq3T1YMXkor1QTyc+N6E4vC/0vhT6lFYPyvdv61WYQ99TnxOTtQp2V+ljavaD4TfD\n+PshvLdn4hOiBhmFGfwp7k9sS9tWthbkF8RTE55iarepHoxMNLdzgz8WxSWzNikTm92J1WLmkv6R\nzJsUw7DooLKqPpvdyTcJaSzdeoztR6u/VPI1m7hiSCduie3OmB4hUg0ohBDCIxqSEHwcuAoYBew3\nDOMtYANwsvR4F2ASMA+IAApLbyOEdwgM14mji34P+7+CbYvgaFz58fQE+PJ3sOoxPahkzNwL76GW\nulMnApPXVV73bQ8X/VZP4/Rrf2GPIfRW8Sueg4kP6sEH2/8N9gJ9LOsAfPYrWPcsBIRCRqJOAFal\nXDrplfSVTqzNWlyeODubUWXIx1695bbqcIXahPTQCb+Kyb/g7o235bLKFFdH0teYXXYMSz2muIb2\n1EnxsfN09eCxH+Dgaji0SlcKVpR1QF+2vA6WQF092Ge6ThKGyBa9VuH0UT3Q6Mf/MKDqdnNrsP4Z\nGHs3tIv0SHhCnE9cShyPbnyUHFtO2drIyJE8P+l5OgV28mBkornZnS4eXBbP6sQMih1OXKWtdIvs\nTlYmnGJNUgbTB0by20v6sGzHCT7ZmcLpwuq/u3uEBTAnthvXj+pKaKBUlgohhPCseicElVIHDMO4\nEvgvOuH3p9JLVQZ6q/BspdTBGo4L0baZffQ20oEzdWJn21t6SMW5F8S2PNj8mn6h3PdyGPsrPcH2\nXOLGnS2nWQdhzVO66qzSY/vpZMvFD0JgWPM+b2/QLhIue0onfbe8DlvfKp/cfPpIDdsga+AorRb8\ndJ7u8ZeWAAX16K5gCSzv9ddpsN72GzkArB0a9pzqo8IU16+++AKAmTNnNuy+LFa9Pbj3dOA5yEku\nTw4e2VBeiQk6+Xpgpb4AhPcrn1zcbbxUD7Y0J3/UFbWJn1euJAXdvmD8b/SbIvWtVBWiidmddl79\n8VXe3ftu2ZqBwd3D7ubuoXfjY5IhD95EKcWDy+JZlZiOze6qdtyldGLwq59O8eXuU9WO+5gMLhvU\nkTljuzOhVxgmk1QDCiGEaBka9BeNUmqDYRj9gPuBWcAg4FzzLCewF50wfF0pVb1rrhDepuMguOoV\nmP4E/LgUti/SVTMAqPItkmG9dWJw8PWw8qHzbzk98C206wi5x6g08dUwwYhbYfIfISi6WZ+iVwoM\ng2mP6QmoW9/UPfBsee7f3lkCe/5b93nB3StX/HUcBCE92+aghdAYiP2VvtiL4OhGnRw8+J1OFlaU\ntV9fNr+mE6Qxk3VysPelENzVM/F7O6Xg8Pew8RWd0K0i178bhyKvZPTtC+reLi+EB5w4c4KH1j9E\nQnZ5m+xI/0iem/QcYzqN8WBkwlPiT+SyOjGjxmRgRS5V+XpUsD83j+3K7NFdiewgw7KEEEK0PA1+\ni7M00fcU8JRhGBYgtPRQjlKqHvvbhPAi/iF6S+W4e3WSY+ub+sXzOdmHdCLwm9Ki25qGeSuXri7L\nPVp5feA1cMmjekKuaF7+ITDlYf11fSGmflt8K7IE6N5+ZYm/wdBxIFiDGjfe1sLir7cH95kOVzwP\n2YdLew9+B0d/qF49uP9rfQGIGFC+tbjbePCRrVlNymnXA5U2/UNvd68qZipc9FvW78kHw2C0JANF\nC/TNkW94cvOTnLWfLVubGDWRBRcvINQaWsstRVu2OO4IxY4a/h47j47t/Xhu1lAm9Y3ALNWAQggh\nWrBG2fNQmgBMb4z7EsIrmEx6m3DfyyHrkK4Y/HFp+bbTmhKB5xM1Gn72EnQZ0TSxCvdZO9Rvyu85\nNyyBTkN0/z+Z/nx+Yb30Jfbu0urBH3Ry8OCq6tu0M/fpy6Z/6KEqMVPKJxfXVT3blBOi25riM7Bz\nCWz5J+SnVD5mmGHwdbqCtvMwvZbwRfPHKEQdihxFPL/teZYfXF625mPy4fcjf89tA2/DZLTBamzh\ntu+T0qtV/9Um3+Zgan/piSqEEKLlu+CEoGEYHYGuQIBSqvr+ICFE7cJ76+qnSx6F3R/B6ifLE4N1\nMvQEXEkGthwWa83DRM57fgAMuqbp4mmrLP7l/QNBVw+eSw4e/QGcxeXnlpyFpP/pC+gqzN7Toc9l\n0G1c5a2rTnvjTYhuy86kwdZ/wfa3objKNnlLIIy8Hcbfq38+CdGCHTx9kPnr53M473DZWnS7aF6c\n/CKDwwd7MDLhSWl5Nr7dm8bKhFN1bhWuylaPakIhhBDCkxqcEDQM40bgEXT/QNCNzHwqHA9G9xE0\ngGuVUu5mOITwTn7t9SCQVX+px41K+w+KlqPP5TpxVHWIQk0Mk57MKy5cWC8I+7WeqF1SqCd7H1yl\nt+aX9esslZGoL5te1ZO4Yybr5GCvabD6cT0dvL4Tor1F5gH97/bTx7oHZkWBEbp6c/Qv9aRtIVow\npRTLDy7nuW3PUVzhDYQrelzBY+Mfo52vDLvxNidyClmZcIqVCWn8eLzhLdCtPlLpL4QQonVoUELQ\nMIzngPnoZF8xYCn9vIxSKtcwjDRgDnA1sPTCQhXCS9htdZ9TkaMe1Wii6U24v3SbaWHd5/pYYfx9\nTR+Tt/ENKN+Sr5TuzXkuOXh0Y5XqwTOVqwcxqDSopyaOIl1BmLpLT1v2Bse36EEh53o0VhTWW38f\nD7tZV8gK0cKdKTnDk5uf5Nuj35atWc1W/hz7Z67pfQ2Gt9sOMxoAACAASURBVCX6vdihjDOs3JPG\nN3vT2Hsy/4Lvz2TAtAGyXVgIIUTrUO+EoGEYlwEPAXnAPOAzIAWo6bffEuAW4FokISiEe+q75dTH\nv+liEfUXNUpvKU36qvZkrY+/Pi9qZPPF5o0MQw/aCe+jt7CWFMCRuPLJxbnHq9zAzUZRDpuulJu9\npNFDbnLu9kh0uXS15MZXIWVb9fuJHgsX/VZ/H0vvS9FK/JT5Ew9teIjUs6lla72De/PS5JfoFdzL\ng5GJ5qCUIvFUPt8kpLEyIY1DGWdrPM9sMhgXE8qMQZ2ICvbnNx/8SJG97q3Afj5m5k6MaeywhRBC\niCbRkArB+9CvmOYrpT4BansndXPpufKKVwh3yZbT1s0wdH+5mvrQgf6a+VjL+9BJJUrz8g2EfjP0\nRSnIOlieHExe5/79KBckfg7PROsp0/5BYA0G/+DSjyEVPq9hzRrkmSSaOz0S+1wGPSbC1n/q6sqq\n+l0JF/1O918UopVwKRdL9i7h1V2v4lDlw59m953N/DHzsfpIdWtb5XIp4lNy+SYhjW8S0jieU3MF\nv8VscHHvcK4Y3JnpAzsSGqin0yulmD4wklWJ6bX2E7RaTEwfGMmw6KAmeR5CCCFEY2tIQjC29OMH\ndZ2olCowDCMP6NSAxxHCO8mW09bPbNH95VJ3lVdhOYp0VWDfGTDhPl1JKDzLMCCir76M/w08EYzb\nFYLnlJzRl7y6T63y4ODXQScIa0scVvzoH6I/9+ugJ5XXl1KlycA6eiQmfq4vFZl9YeiN+udTRL/6\nP7YQHpRdlM0jGx9hY+rGsrX2lvY8PuFxLu9xuQcjE03F6VJsP5pTlgRMy6+5HYvVYmJK30iuGNKJ\nqf0j6WCtPizKMAxenj2cB5fFszoxg2KHs9LUYZOhKwOnD4zk5dnDZcu5EEKIVqMhCcFgIF8p5Ua2\nAgDZRyREfciW07bBMHR/uda4pdRb1Xe7/gVRejpvcR7kHqvfTQ1ThWRiyPkTh1WTiqeP6srA+jxH\nvyAYcxfE3gPt5b090fpsObWFP8X9iayirLK1oeFDeX7S80S3j/ZgZKKxOVyw/kAm3ySc4ru96WQX\nlNR4Xjs/H6YNiGTGoE5M7hdBgG/dL4csZhOv3jSC3Sl5LNqQzJqkDGwOJ1YfM9MGRDJvYgzDugY3\n9lMSQgghmlRDEoI5QKRhGAF1JQUNw+gJtAeONuBxhPBOsuVUCM+o73b9ATPhqr9BUS7Ycit8PF3H\nWmkisKGUS9+PLbf6BOXG1HEw3PWNnoAuRCvjcDl4I/4NFu9ZjKpQ+XvX4Lu4b8R9WEzVK8FE62Oz\nO9lwIJP/HDKRkGNQtLWGfqdAcICFSwd05Iohnbiodzh+DZgEbBgGw7sG8/ot8kasEEKItqEhCcFt\nwM9LL8vqOPcPpR/jGvA4Qngv2XIqRPOr73b9CfeXbu8Nqf9juZxgy9OJwjqTiVU+LzlT/8driJxk\nSQaKVunU2VP8Me6P/JjxY9laqDWUZy9+lglREzwYmWgMBcUO1u7PYGVCGmuTMigscQLV2yhEtPfj\n8kEduWJwZ8b2DMVibkCrBSGEEKINa0hCcDFwFfCMYRhblVLV9joZhmEG/gTci27I9K8LilIIbyRb\nToVoXs25Xd9khoBQfakvp700mVhHZWLVZGN+at33XVFt/wZCtFDfH/+exzY+Rn5JftnauM7jeHbi\ns4T7h3swMnEh8grtrN6XzsqENDYczKTEUXMld1SwPzMGd2LG4E6M7BaC2SS7KIQQQojzqXdCUCn1\npWEYHwBzgF2GYXwOBAIYhnEfMBCdMOxSepN/KqU2N1K8QgghRNNoLdv1zRYIDNeX+ni6U/36B/r4\n1+/+hfCgYmcxC3cs5MOkD8vWzIaZ+0bcx12D78JkSHVYa5N9tpjvEnUScNOhLByumoc+9QwPpJfv\nGYaFubhvzlQZ6iGEEEK4qSEVggB3ApnA/cAvStcU8Erp5wbgAl4G/ngB8QkhhBDNpy1v169vj8S+\nM5o+JiEawZG8Izy04SGScpLK1joHduaFSS8wPHK4ByMT9ZWWZ+ObhFOsTEhj+9EczpMDpH+n9swY\n3IkrBnemb8d2rFixAkCSgUIIIUQ9NCghqJRyAA8YhvE6cAcwHuiMbuCRDmwGliilks5/L0IIIUQL\n1Fa369e3R+L4+5o+JiEu0IrDK1iwZQFFFba4T+s2jScnPEmQX5AHI/M+SiniT+SyKC6ZtUmZ2OxO\nrBYzl/SPZN6kGIZFB9WYsDueXcg3e3US8Mfjuee9/6HRQWVJwJ7hgU35VIQQQgiv0NAKQQCUUoeA\nvzRSLEIIIYRoKs3ZI1GIJlZgL+DpLU/zZfKXZWu+Jl/mj5nPjf1ulEqxZmZ3unhwWTyrEzModjjL\nKvuK7E5WJpxiTVIG0wdG8vLs4VjMJg5lnGHlnjRWJqSReCq/xvs0DBjdPYQZgzszY3AnooKljYEQ\nQgjRmC4oISiEEEKIVqK19EgUog77svcxf8N8juWXz7Xr0aEHL01+iX6h/TwYmXdSSvHgsnhWJaZj\ns1dvSeBSOjH47d50rnwlDqfLRXJWzZXKZpPB+JgwZgzuxGWDOhLZ3trU4QshhBBeSxKCQgghhLdo\nyz0SRaunlGJP1h7e3fsu63LXYcfOgv8sYFL0JO4cdCeDwgbx4f4PWbhjIXaXvex2M3vN5M+xfybA\nEuDB6L1X/IlcVidm1JgMrKjE4eJgxtlq675mExf3CWfG4E5cOqAjIYG+TRWqEEIIISqoMyFoGMbt\njfFASqn3GuN+hBBCCHEB2mqPRNGq2V12Hol7hLUn1lLiLMGFTi7ZnDZWH1vNhpQNdPDtQEZRRtlt\nAnwCeHTco1zV6ypPhS2AxXFHKHY463Ubf4uZKf0imDG4E5f0j6S91dJE0QkhhBDifNypEHwXPUH4\nQihAEoJCCCGEEKISpVRZMtDmtFU77sKFzWnDVlR+bEDoAF6c/CLdO3RvzlBFFTa7k1X70s87Dbgm\nvmYTu/5yKf6+5qYLTAghhBB1qs+W4dNALV3IhRBCCCGEqJ89WXtYl7KuxmRgTWb0mMHTFz+Nr1m2\nljY3p0uRkJrHxsNZbDqUzfajOZQ4at8qXJXd5ZJkoBBCCNEC1CchaAFWAO8ppdY1TThCCCGEEMKb\nLNm7hGJHsVvnGhgolCQDm4lSiuSsAjYeymLjoSw2H84m3+a4oPu0+kgyUAghhGgJ3EkIXgfcBvwM\nuBO4wzCM48BSdHLwQNOFJ4QQQgghWju7005WURaZRZlkFmWSVag/zyrKYvXx1WU9A+uiUGxI2dDE\n0Xq3tDybTgCWVgGm5ddeuRnoa6agxL0egiYDpg2IbIwwhRBCCHGB6kwIKqU+Bz43DCMEuBm4HRgL\n/Bn4k2EYO4AlwEdKqZymDFYIIYQQwptVnMQblxJHsbMYP7Nf2STeweGDMQyj2eIptBfqJF9h5nkT\nfllFWeQW5zbaY9oc7m0tFu7JK7KzJTm7rArwcGZBredHtvfj4t7hTOgdzkW9w0jLszFn0VaK7HUn\nBf18zMydGNNYoQshhBDiAri9ZVgpdRp4A3jDMIw+wB3ALcAYYDTwsmEYXwP/Ab5UStmbIF4hhBBC\nCK9U1yTeuNQ4pkRP4emJT2MxNXxqq1KKvOK88uReUValhN+5JF9mYSaFjv9n777Do6yyB45/35lM\nKpAASQBTCCXU0EEpSUhBQFdFUEBUBCyIdV111SUWFKPgT1gXUEBUwMWyWFgUF5Q0Eoqg1ISi9BRq\ngNCSSabc3x8ThgRCCiSZlPN5Hp8wd+77vmdgMs6cOfee3Mp6eOXm6uRa7desS4wmC5sPn7EnAFOz\nzpbaFKShqxN9WzcltDAB2ManQbGkc/NGrgzq5MvqXccxmq5d6elq0DGoky/d/D0r8+EIIYQQ4jpV\nZA9BO6XUXuBV4FVN0wZiSw6OAO4GhgFnNE17Tim1pNIiFUIIIYSop8rTiTfPnEdiRiIxKTFMD59+\nVaWg2WrmtPF0sQq+k3knOZV36qqEn8laud/r6jQdTV2b4u3mjY+7Dz5uPni7eePt5s1PB35i+8nt\nKMpuVatDR7h/eKXGVtdZrIrUrLOs25fN+v3Z/HboTKmNQJyddPRu2ZgBbb0Z0NabkJsa4aTXXXO+\npmnMHNWd55duI27XCfLNlmIJRp1mqwwc1MmXmaO6V2sFqxBCCCGu7boSgkUppdYAazRNexJ4GogF\nGgNdb/TcQgghhBCi/J14jRYjcelxvLbuNXSarliF35n8M1hVxTrClsVZ54yPuw9N3Zrak3w+bj74\nuBf/c2OXxuh1JTeT6NS0E4/+8ih55ryyr6d3ZlyncZX6GOoapRT7T15g3T7bMuBfD5TeCETToKuf\np20JcBtvegc1xtVQscYfBr2OWff1YHvmWRYkHyBhzwmMZguuTnqiO/ryWFhrugV43ehDE0IIIUQl\nuuGEoKZpbsA92BqPRAGX3kGcv9FzCyGEEEKIinXiNVlNLN+//Iau52HwKJbg83b3vny7SIVfI+dG\nN1zx1cW7CxH+EdesfrzEVe9KZEAkId4hN3S9uujo2TzW7TvF+sJmIMfPlf5cae3jYdsHsI03/Vo3\nxdP9+peYX6JpGt0DvPjwgZ43fC4hhBBCVL3rTghqmhaFrcHICMAD0IATwNfYug9vqZQIhRBCCCHq\nueTM5HJ34i1NY5fGxZN7V1bzudmq/dwN7pUQdflomkZsWCwxKTEkZSaRb84v9lh16HDWOxMZEEls\nWKwsOQXO5prYcKkRyP5sDpTRCKRZIxcGtLEtAe7ftiktPN2qKVIhhBBC1FQVSghqmtYRWxLwAcAP\nWxLQCHwLfA6sUkqV3WJMCCGEEEKUW76lfNWBRb3W97ViCb+mrk0x6G+8EqwqGHQGpodPJy07jUU7\nF5F4OBEzZlydXIt1UK7NlFJsy8hhQcoBEvecxGiy4GrQE9XBl8fCW9PN3/OayU6jycLvh86wb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hblSdTggCKKWWAcsqMN+KrZqvQhV9hcd+CVwzaVjC/IhyzJkNzK5oLNfDw+DBlP5TiA6M5o31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\n9/f+wHBgX+AY4BsRcSvw+ZTSglaO766uBwa196DNvUdJ6tU2534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nbvR6iyLiMLJwDOCT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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0423e9860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"poster\")\n",
    "ax=df_BayArea_T.plot(kind='line', x='MonthYr', figsize=(20,12), grid=True, style='o-')\n",
    "ax.yaxis.set_major_formatter(tick.FuncFormatter(fmt_x))\n",
    "plt.xlabel(\"Mon 'Yr\", labelpad=20)\n",
    "plt.ylabel(\"Median Sale Price (in $)\", labelpad=20)\n",
    "plt.title(\"Median Price growth in Top Metros of Bay Area - Sept 2008 to Jun 2018\")    \n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis : Bay area cities a much higher percentage increase in their Median Sale price as compared to Nation increase \n",
    "           San Jose - 150%\n",
    "           Santa Clara - 156%\n",
    "           San Francisco - 101%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Home Sale Counts by state - US Map <a class=\"anchor\" id=\"plot5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>2008-03</th>\n",
       "      <th>2008-04</th>\n",
       "      <th>2008-05</th>\n",
       "      <th>2008-06</th>\n",
       "      <th>2008-07</th>\n",
       "      <th>2008-08</th>\n",
       "      <th>2008-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>23518.0</td>\n",
       "      <td>28536.0</td>\n",
       "      <td>31373.0</td>\n",
       "      <td>33071.0</td>\n",
       "      <td>36669.0</td>\n",
       "      <td>35316.0</td>\n",
       "      <td>36477.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41189</td>\n",
       "      <td>37759</td>\n",
       "      <td>36593</td>\n",
       "      <td>29333</td>\n",
       "      <td>29312</td>\n",
       "      <td>39767</td>\n",
       "      <td>40783</td>\n",
       "      <td>44718</td>\n",
       "      <td>44630.0</td>\n",
       "      <td>43208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>Texas</td>\n",
       "      <td>2</td>\n",
       "      <td>23774.0</td>\n",
       "      <td>25876.0</td>\n",
       "      <td>26542.0</td>\n",
       "      <td>27791.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>25213.0</td>\n",
       "      <td>21392.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32804</td>\n",
       "      <td>28839</td>\n",
       "      <td>29941</td>\n",
       "      <td>24783</td>\n",
       "      <td>25329</td>\n",
       "      <td>31152</td>\n",
       "      <td>32593</td>\n",
       "      <td>38935</td>\n",
       "      <td>39184.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>New York</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19289</td>\n",
       "      <td>17409</td>\n",
       "      <td>17860</td>\n",
       "      <td>16239</td>\n",
       "      <td>13335</td>\n",
       "      <td>14014</td>\n",
       "      <td>14461</td>\n",
       "      <td>17680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>Florida</td>\n",
       "      <td>4</td>\n",
       "      <td>17429.0</td>\n",
       "      <td>19501.0</td>\n",
       "      <td>19766.0</td>\n",
       "      <td>20462.0</td>\n",
       "      <td>20923.0</td>\n",
       "      <td>17640.0</td>\n",
       "      <td>17759.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38908</td>\n",
       "      <td>36071</td>\n",
       "      <td>40928</td>\n",
       "      <td>36048</td>\n",
       "      <td>34492</td>\n",
       "      <td>45172</td>\n",
       "      <td>46433</td>\n",
       "      <td>49560</td>\n",
       "      <td>48507.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>5</td>\n",
       "      <td>11823.0</td>\n",
       "      <td>13158.0</td>\n",
       "      <td>13967.0</td>\n",
       "      <td>15312.0</td>\n",
       "      <td>16499.0</td>\n",
       "      <td>15070.0</td>\n",
       "      <td>14189.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19981</td>\n",
       "      <td>18048</td>\n",
       "      <td>17525</td>\n",
       "      <td>14702</td>\n",
       "      <td>12155</td>\n",
       "      <td>17074</td>\n",
       "      <td>19639</td>\n",
       "      <td>23725</td>\n",
       "      <td>27333.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID  RegionName  SizeRank  2008-03  2008-04  2008-05  2008-06  \\\n",
       "0         9  California         1  23518.0  28536.0  31373.0  33071.0   \n",
       "1        54       Texas         2  23774.0  25876.0  26542.0  27791.0   \n",
       "2        43    New York         3      NaN      NaN      NaN      NaN   \n",
       "3        14     Florida         4  17429.0  19501.0  19766.0  20462.0   \n",
       "4        21    Illinois         5  11823.0  13158.0  13967.0  15312.0   \n",
       "\n",
       "   2008-07  2008-08  2008-09   ...     2017-10  2017-11  2017-12  2018-01  \\\n",
       "0  36669.0  35316.0  36477.0   ...       41189    37759    36593    29333   \n",
       "1  28239.0  25213.0  21392.0   ...       32804    28839    29941    24783   \n",
       "2      NaN      NaN      NaN   ...       19289    17409    17860    16239   \n",
       "3  20923.0  17640.0  17759.0   ...       38908    36071    40928    36048   \n",
       "4  16499.0  15070.0  14189.0   ...       19981    18048    17525    14702   \n",
       "\n",
       "   2018-02  2018-03  2018-04  2018-05  2018-06  2018-07  \n",
       "0    29312    39767    40783    44718  44630.0  43208.0  \n",
       "1    25329    31152    32593    38935  39184.0      NaN  \n",
       "2    13335    14014    14461    17680      NaN      NaN  \n",
       "3    34492    45172    46433    49560  48507.0      NaN  \n",
       "4    12155    17074    19639    23725  27333.0      NaN  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Counts_State = pd.read_csv(\"Home Sale Counts\\Sale_Counts_State.CSV\")\n",
    "df_Counts_State.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>RegionName</th>\n",
       "      <th>2008-03</th>\n",
       "      <th>2008-04</th>\n",
       "      <th>2008-05</th>\n",
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       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
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       "      <th>Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>23518.0</td>\n",
       "      <td>28536.0</td>\n",
       "      <td>31373.0</td>\n",
       "      <td>33071.0</td>\n",
       "      <td>36669.0</td>\n",
       "      <td>35316.0</td>\n",
       "      <td>36477.0</td>\n",
       "      <td>38863.0</td>\n",
       "      <td>29342.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>36593</td>\n",
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       "      <td>36122.296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Texas</td>\n",
       "      <td>23774.0</td>\n",
       "      <td>25876.0</td>\n",
       "      <td>26542.0</td>\n",
       "      <td>27791.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>25213.0</td>\n",
       "      <td>21392.0</td>\n",
       "      <td>21819.0</td>\n",
       "      <td>15415.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28839</td>\n",
       "      <td>29941</td>\n",
       "      <td>24783</td>\n",
       "      <td>25329</td>\n",
       "      <td>31152</td>\n",
       "      <td>32593</td>\n",
       "      <td>38935</td>\n",
       "      <td>39184.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25739.443548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>17409</td>\n",
       "      <td>17860</td>\n",
       "      <td>16239</td>\n",
       "      <td>13335</td>\n",
       "      <td>14014</td>\n",
       "      <td>14461</td>\n",
       "      <td>17680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13604.477778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>17429.0</td>\n",
       "      <td>19501.0</td>\n",
       "      <td>19766.0</td>\n",
       "      <td>20462.0</td>\n",
       "      <td>20923.0</td>\n",
       "      <td>17640.0</td>\n",
       "      <td>17759.0</td>\n",
       "      <td>18072.0</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36071</td>\n",
       "      <td>40928</td>\n",
       "      <td>36048</td>\n",
       "      <td>34492</td>\n",
       "      <td>45172</td>\n",
       "      <td>46433</td>\n",
       "      <td>49560</td>\n",
       "      <td>48507.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31568.653226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>11823.0</td>\n",
       "      <td>13158.0</td>\n",
       "      <td>13967.0</td>\n",
       "      <td>15312.0</td>\n",
       "      <td>16499.0</td>\n",
       "      <td>15070.0</td>\n",
       "      <td>14189.0</td>\n",
       "      <td>13581.0</td>\n",
       "      <td>9376.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18048</td>\n",
       "      <td>17525</td>\n",
       "      <td>14702</td>\n",
       "      <td>12155</td>\n",
       "      <td>17074</td>\n",
       "      <td>19639</td>\n",
       "      <td>23725</td>\n",
       "      <td>27333.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15204.161290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionName  2008-03  2008-04  2008-05  2008-06  2008-07  2008-08  2008-09  \\\n",
       "0  California  23518.0  28536.0  31373.0  33071.0  36669.0  35316.0  36477.0   \n",
       "1       Texas  23774.0  25876.0  26542.0  27791.0  28239.0  25213.0  21392.0   \n",
       "2    New York      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3     Florida  17429.0  19501.0  19766.0  20462.0  20923.0  17640.0  17759.0   \n",
       "4    Illinois  11823.0  13158.0  13967.0  15312.0  16499.0  15070.0  14189.0   \n",
       "\n",
       "   2008-10  2008-11      ...       2017-11  2017-12  2018-01  2018-02  \\\n",
       "0  38863.0  29342.0      ...         37759    36593    29333    29312   \n",
       "1  21819.0  15415.0      ...         28839    29941    24783    25329   \n",
       "2      NaN      NaN      ...         17409    17860    16239    13335   \n",
       "3  18072.0  13673.0      ...         36071    40928    36048    34492   \n",
       "4  13581.0   9376.0      ...         18048    17525    14702    12155   \n",
       "\n",
       "   2018-03  2018-04  2018-05  2018-06  2018-07           Avg  \n",
       "0    39767    40783    44718  44630.0  43208.0  36122.296000  \n",
       "1    31152    32593    38935  39184.0      NaN  25739.443548  \n",
       "2    14014    14461    17680      NaN      NaN  13604.477778  \n",
       "3    45172    46433    49560  48507.0      NaN  31568.653226  \n",
       "4    17074    19639    23725  27333.0      NaN  15204.161290  \n",
       "\n",
       "[5 rows x 127 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Counts_State = df_Counts_State.drop(['RegionID','SizeRank'], axis=1)\n",
    "df_Counts_State['Avg'] = df_Counts_State.mean(axis=1)\n",
    "df_Counts_State.head()\n",
    "df_state_MeanCount = df_Counts_State.ix[:, ['RegionName','Avg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>Avg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>36122.296000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Texas</td>\n",
       "      <td>25739.443548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New York</td>\n",
       "      <td>13604.477778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>31568.653226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>15204.161290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionName           Avg\n",
       "0  California  36122.296000\n",
       "1       Texas  25739.443548\n",
       "2    New York  13604.477778\n",
       "3     Florida  31568.653226\n",
       "4    Illinois  15204.161290"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state_MeanCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly as ply\n",
    "import pandas as pd\n",
    "\n",
    "#for col in df_persqft_state.columns:\n",
    " #   df_persqft_state['Increase'] = df_persqft_state['Diff'].astype(str)\n",
    "\n",
    "scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\\\n",
    "            [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]\n",
    "\n",
    "\n",
    "df_state_MeanCount['text'] = df_state_MeanCount['RegionName'] + '<br>' +\\\n",
    "    'Avg Count: '+df_state_MeanCount['Avg'].astype(str)\n",
    "    \n",
    "data = [ dict(\n",
    "        type='choropleth',\n",
    "        colorscale = scl,\n",
    "        autocolorscale = False,\n",
    "        locations = df_state_MeanCount['RegionName'],\n",
    "        z = df_state_MeanCount['Avg'].astype(float),\n",
    "        locationmode = 'USA-states',\n",
    "        text = df_state_MeanCount['text'],\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(255,255,255)',\n",
    "                width = 2\n",
    "            ) ),\n",
    "        colorbar = dict(\n",
    "            title = \"Avg Count\")\n",
    "        ) ]\n",
    "\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Avg Count of Houses Sold Monthly (2010 - 2018)',\n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict( type='albers usa' ),\n",
    "            showlakes = True,\n",
    "            lakecolor = 'rgb(255, 255, 255)'),\n",
    "             )\n",
    "#ply.tools.set_credentials_file(username='singuava', api_key='k4gITYpzzdFXRtcEcahg')\n",
    " \n",
    "#Click anywhere on the image below to be taken to plotly page with this plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "    <a href=\"https://plot.ly/~singuava/8/?share_key=t42budrsWYDdGzeUXaF7fS\" target=\"_blank\" title=\"Avg HousesSold - 2\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~singuava/8.png?share_key=t42budrsWYDdGzeUXaF7fS\" alt=\"Avg HousesSold - 2\" style=\"max-width: 100%;width: 600px;\"  width=\"600\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
    "    <script data-plotly=\"singuava:8\" sharekey-plotly=\"t42budrsWYDdGzeUXaF7fS\" src=\"https://plot.ly/embed.js\" async></script>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis  : California, Texas, Florida are 3 popular states in terms of Avg. houses sold per month. They all range from 25000 to 36000 avg. number of houses sold per month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Days On Market  - Seasonality Trend over the years  <a class=\"anchor\" id=\"plot6\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>RegionType</th>\n",
       "      <th>StateFullName</th>\n",
       "      <th>DataTypeDescription</th>\n",
       "      <th>2012-01</th>\n",
       "      <th>2012-02</th>\n",
       "      <th>2012-03</th>\n",
       "      <th>2012-04</th>\n",
       "      <th>2012-05</th>\n",
       "      <th>2012-06</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>Country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All Homes</td>\n",
       "      <td>119</td>\n",
       "      <td>114</td>\n",
       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>Msa</td>\n",
       "      <td>New York</td>\n",
       "      <td>All Homes</td>\n",
       "      <td>136</td>\n",
       "      <td>126</td>\n",
       "      <td>112</td>\n",
       "      <td>86</td>\n",
       "      <td>81</td>\n",
       "      <td>86</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>78</td>\n",
       "      <td>51</td>\n",
       "      <td>55</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Msa</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>All Homes</td>\n",
       "      <td>140</td>\n",
       "      <td>135</td>\n",
       "      <td>122</td>\n",
       "      <td>98</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>86</td>\n",
       "      <td>98</td>\n",
       "      <td>105</td>\n",
       "      <td>88</td>\n",
       "      <td>50</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "      <td>54</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RegionName RegionType StateFullName DataTypeDescription  2012-01  \\\n",
       "0  United States    Country           NaN           All Homes      119   \n",
       "1   New York, NY        Msa      New York           All Homes      136   \n",
       "2    Chicago, IL        Msa      Illinois           All Homes      140   \n",
       "\n",
       "   2012-02  2012-03  2012-04  2012-05  2012-06   ...     2017-10  2017-11  \\\n",
       "0      114      102       84       78       80   ...          81       85   \n",
       "1      126      112       86       81       86   ...          82       84   \n",
       "2      135      122       98       88       88   ...          78       86   \n",
       "\n",
       "   2017-12  2018-01  2018-02  2018-03  2018-04  2018-05  2018-06  2018-07  \n",
       "0       92       98       91       67       60       57       58       62  \n",
       "1       92       99       78       51       55       53       58       66  \n",
       "2       98      105       88       50       46       50       54       60  \n",
       "\n",
       "[3 rows x 83 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age = pd.read_csv(\"Inventory\\AgeOfInventory_Metro_Public.CSV\")\n",
    "df_age.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_age = df_age[(df_age['RegionName']=='San Francisco, CA')  | (df_age['RegionName']=='United States')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>RegionType</th>\n",
       "      <th>StateFullName</th>\n",
       "      <th>DataTypeDescription</th>\n",
       "      <th>2012-01</th>\n",
       "      <th>2012-02</th>\n",
       "      <th>2012-03</th>\n",
       "      <th>2012-04</th>\n",
       "      <th>2012-05</th>\n",
       "      <th>2012-06</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>Country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All Homes</td>\n",
       "      <td>119</td>\n",
       "      <td>114</td>\n",
       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>Msa</td>\n",
       "      <td>California</td>\n",
       "      <td>All Homes</td>\n",
       "      <td>82</td>\n",
       "      <td>60</td>\n",
       "      <td>42</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>46</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           RegionName RegionType StateFullName DataTypeDescription  2012-01  \\\n",
       "0       United States    Country           NaN           All Homes      119   \n",
       "10  San Francisco, CA        Msa    California           All Homes       82   \n",
       "\n",
       "    2012-02  2012-03  2012-04  2012-05  2012-06   ...     2017-10  2017-11  \\\n",
       "0       114      102       84       78       80   ...          81       85   \n",
       "10       60       42       39       35       40   ...          25       34   \n",
       "\n",
       "    2017-12  2018-01  2018-02  2018-03  2018-04  2018-05  2018-06  2018-07  \n",
       "0        92       98       91       67       60       57       58       62  \n",
       "10       46       26       15       14       14       15       20       24  \n",
       "\n",
       "[2 rows x 83 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>2012-01</th>\n",
       "      <th>2012-02</th>\n",
       "      <th>2012-03</th>\n",
       "      <th>2012-04</th>\n",
       "      <th>2012-05</th>\n",
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       "      <th>2012-07</th>\n",
       "      <th>2012-08</th>\n",
       "      <th>2012-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
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       "      <th>2018-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>119</td>\n",
       "      <td>114</td>\n",
       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "      <td>86</td>\n",
       "      <td>91</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>91</td>\n",
       "      <td>67</td>\n",
       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>82</td>\n",
       "      <td>60</td>\n",
       "      <td>42</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>46</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           RegionName  2012-01  2012-02  2012-03  2012-04  2012-05  2012-06  \\\n",
       "0       United States      119      114      102       84       78       80   \n",
       "10  San Francisco, CA       82       60       42       39       35       40   \n",
       "\n",
       "    2012-07  2012-08  2012-09   ...     2017-10  2017-11  2017-12  2018-01  \\\n",
       "0        86       91       94   ...          81       85       92       98   \n",
       "10       38       40       36   ...          25       34       46       26   \n",
       "\n",
       "    2018-02  2018-03  2018-04  2018-05  2018-06  2018-07  \n",
       "0        91       67       60       57       58       62  \n",
       "10       15       14       14       15       20       24  \n",
       "\n",
       "[2 rows x 80 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age = df_age.drop(['RegionType','StateFullName','DataTypeDescription'], axis=1)\n",
    "df_age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>RegionName</th>\n",
       "      <th>index</th>\n",
       "      <th>United States</th>\n",
       "      <th>San Francisco, CA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03</td>\n",
       "      <td>102</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04</td>\n",
       "      <td>84</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05</td>\n",
       "      <td>78</td>\n",
       "      <td>35</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RegionName    index  United States  San Francisco, CA\n",
       "0           2012-01            119                 82\n",
       "1           2012-02            114                 60\n",
       "2           2012-03            102                 42\n",
       "3           2012-04             84                 39\n",
       "4           2012-05             78                 35"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_T = df_age.set_index('RegionName').T\n",
    "df_age_T.reset_index(level=0,inplace=True)\n",
    "df_age_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##Name columns\n",
    "df_age_T.columns = ['YearMonth','United States', 'San Francisco, CA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>United States</th>\n",
       "      <th>San Francisco, CA</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02</td>\n",
       "      <td>114</td>\n",
       "      <td>60</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03</td>\n",
       "      <td>102</td>\n",
       "      <td>42</td>\n",
       "      <td>2012</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04</td>\n",
       "      <td>84</td>\n",
       "      <td>39</td>\n",
       "      <td>2012</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05</td>\n",
       "      <td>78</td>\n",
       "      <td>35</td>\n",
       "      <td>2012</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearMonth  United States  San Francisco, CA  Year Month\n",
       "0   2012-01            119                 82  2012    01\n",
       "1   2012-02            114                 60  2012    02\n",
       "2   2012-03            102                 42  2012    03\n",
       "3   2012-04             84                 39  2012    04\n",
       "4   2012-05             78                 35  2012    05"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Make a new column Year from YearMonth\n",
    "df_age_T['Year']= (df_age_T['YearMonth'].str.split('-').str[0])\n",
    "df_age_T['Month']= (df_age_T['YearMonth'].str.split('-').str[1])\n",
    "df_age_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>United States</th>\n",
       "      <th>San Francisco, CA</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02</td>\n",
       "      <td>114</td>\n",
       "      <td>60</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03</td>\n",
       "      <td>102</td>\n",
       "      <td>42</td>\n",
       "      <td>2012</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04</td>\n",
       "      <td>84</td>\n",
       "      <td>39</td>\n",
       "      <td>2012</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05</td>\n",
       "      <td>78</td>\n",
       "      <td>35</td>\n",
       "      <td>2012</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearMonth  United States  San Francisco, CA  Year Month\n",
       "0   2012-01            119                 82  2012    01\n",
       "1   2012-02            114                 60  2012    02\n",
       "2   2012-03            102                 42  2012    03\n",
       "3   2012-04             84                 39  2012    04\n",
       "4   2012-05             78                 35  2012    05"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_T_2017 = df_age_T[(df_age_T['Year'].astype(int) < 2018)]\n",
    "df_age_T_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Month_to_MonthName = { '01' : 'Jan', \n",
    "                  '02' : 'Feb', \n",
    "                  '03' : 'Mar', \n",
    "                  '04' : 'Apr', \n",
    "                  '05' : 'May',\n",
    "                  '06' : 'Jun', \n",
    "                  '07' : 'Jul', \n",
    "                  '08' : 'Aug', \n",
    "                  '09' : 'Sep', \n",
    "                  '10' : 'Oct',\n",
    "                  '11' : 'Nov', \n",
    "                  '12' : 'Dec'}\n",
    "df_age_T_2017['MonthName'] = df_age_T_2017['Month'].map(Month_to_MonthName)\n",
    "df_age_T_2017['Yr']=df_age_T_2017['Year'].apply(lambda x: '\\''+x[-2:])\n",
    "df_age_T_2017['MonthYr']=df_age_T_2017['MonthName'] +' '+ df_age_T_2017['Yr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>United States</th>\n",
       "      <th>San Francisco, CA</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>MonthName</th>\n",
       "      <th>Yr</th>\n",
       "      <th>MonthYr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01</td>\n",
       "      <td>119</td>\n",
       "      <td>82</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>Jan</td>\n",
       "      <td>'12</td>\n",
       "      <td>Jan '12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02</td>\n",
       "      <td>114</td>\n",
       "      <td>60</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>Feb</td>\n",
       "      <td>'12</td>\n",
       "      <td>Feb '12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03</td>\n",
       "      <td>102</td>\n",
       "      <td>42</td>\n",
       "      <td>2012</td>\n",
       "      <td>03</td>\n",
       "      <td>Mar</td>\n",
       "      <td>'12</td>\n",
       "      <td>Mar '12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04</td>\n",
       "      <td>84</td>\n",
       "      <td>39</td>\n",
       "      <td>2012</td>\n",
       "      <td>04</td>\n",
       "      <td>Apr</td>\n",
       "      <td>'12</td>\n",
       "      <td>Apr '12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05</td>\n",
       "      <td>78</td>\n",
       "      <td>35</td>\n",
       "      <td>2012</td>\n",
       "      <td>05</td>\n",
       "      <td>May</td>\n",
       "      <td>'12</td>\n",
       "      <td>May '12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  YearMonth  United States  San Francisco, CA  Year Month MonthName   Yr  \\\n",
       "0   2012-01            119                 82  2012    01       Jan  '12   \n",
       "1   2012-02            114                 60  2012    02       Feb  '12   \n",
       "2   2012-03            102                 42  2012    03       Mar  '12   \n",
       "3   2012-04             84                 39  2012    04       Apr  '12   \n",
       "4   2012-05             78                 35  2012    05       May  '12   \n",
       "\n",
       "   MonthYr  \n",
       "0  Jan '12  \n",
       "1  Feb '12  \n",
       "2  Mar '12  \n",
       "3  Apr '12  \n",
       "4  May '12  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_T_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4RgL21BzjsF8qa2PtQIGdrl8Gu+nIQr9nY8xWYh3sePeeKjtshaWrOwe/kHsz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\ntL/S10ORsk4T+BIJSP3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4uyd0GerufpqiJ7OqJUwzvOw1FodI2NnLX8s0UKciaPdqGbYF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d043b33940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"poster\")\n",
    "ax=df_age_T_2017.plot(kind='line', x='MonthYr', figsize=(20,12), grid=True)\n",
    "ax.yaxis.set_major_formatter(tick.FuncFormatter(fmt_x))\n",
    "plt.xlabel(\"[Mon 'Yr]\", labelpad=20)\n",
    "plt.ylabel(\"Number Of Days On Market\", labelpad=20)\n",
    "plt.title(\"Inventory Trend - Avg. Days On Market Trend(2012 - 2017) in USA(Whole) and SF\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis : 1) A clear pattern seasonality pattern can be observed between SF and USA over the years. Winter months (Nov - Feb) are periods of low house buying and hence the 'Days On Market' a property has been 'Active' in market are in the peak. Where as in summer properties tend to get sold faster. Hence the avg. days on market in these months is low.\n",
    "2) The number of Days properties are on market, has decreased over the years from 2012 - 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_age_T_2yrs = df_age_T[(df_age_T['Year']=='2017')]\n",
    "df_age_T_2yrs['MonthName'] =[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>United States</th>\n",
       "      <th>San Francisco, CA</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>MonthName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2017-01</td>\n",
       "      <td>104</td>\n",
       "      <td>47</td>\n",
       "      <td>2017</td>\n",
       "      <td>01</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2017-02</td>\n",
       "      <td>102</td>\n",
       "      <td>26</td>\n",
       "      <td>2017</td>\n",
       "      <td>02</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2017-03</td>\n",
       "      <td>86</td>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>03</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2017-04</td>\n",
       "      <td>68</td>\n",
       "      <td>20</td>\n",
       "      <td>2017</td>\n",
       "      <td>04</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2017-05</td>\n",
       "      <td>62</td>\n",
       "      <td>19</td>\n",
       "      <td>2017</td>\n",
       "      <td>05</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearMonth  United States  San Francisco, CA  Year Month MonthName\n",
       "60   2017-01            104                 47  2017    01       Jan\n",
       "61   2017-02            102                 26  2017    02       Feb\n",
       "62   2017-03             86                 21  2017    03       Mar\n",
       "63   2017-04             68                 20  2017    04       Apr\n",
       "64   2017-05             62                 19  2017    05       May"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_T_2yrs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LALp06UKrVq0Kly1fvpyZM2cyZcoUrr/++sLyU045ha5duzJ9+nRuuOEGvv32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iRJkiRJqjM1KbDuISnFGqPvVH2+cRv39Ae6AXd/5vx9JDPKTgcsxSRJkiRJkpqAapdiMcZv1mGO\nunY08A5wWgjhl8AewBJgLPDLGGMp0Kfq3re2/MIYY3EIYc4W1yVJkiRJktTIhRgb6+Sv6gshvAv0\nAMqBK4C3gcHAL4CHYoxnhxAuBX4LdI4xrvjM1/8LyIkxHr6V116znW/frlWrVjz11FO18JNITdfa\ntWsBaNeuXcpJpIbN94pUPb5XpOrxvSJVj++Vhmfo0KFs2LBhbYyx/Rd9jRrv/xVCGASMAHpVnZoL\nPBJjnPxFQ9SDLKANcGaM8cGqc8+HEFoAPwkh/GqLez+vJWz67aEkSZIkSVKGqHYpFkLIItlv6ywg\nAJVVl7KAC0II9wHnxoY59WwlsCfw9GfOPwn8BDio6h6AjluMN9sJmLe1F95eIxlCWBNCaDdw4MCa\nZpYyyksvvQSA7xVp23yvSNXje0WqHt8rUvX4Xml4srOzd/g1smpw74+Bs4G/AwcCLao+DgAeqrr2\nox1OVDdmfs75zY/PrCRZUgmf2TsshNAS2J3P7DUmSZIkSZKkxqsmpdg3gWdijGfEGKfHGMuqPmbE\nGM8EngW+XScpd9y4qs/DPnN+GMmyyNeBqcBS4JzP3HMm0GyL15AkSZIkSVIjV5M9xXoBf9nG9QnA\ntTsWp27EGJ8KITwJ3BRC6MQnG+3/ELglxlgIEEL4H+CuEMKfSWbE7QP8Dvh7jHFqOuklSZIkSZJU\n22pSihUBXbdxvVvVPQ3VV4H/BX4GdAYWAJcDv998Q4zx7hBCBfBzYDSwArgF+NV/vZokSZIkSZIa\nrZqUYlOAC0MIf4sxvr3lhRDCvsAFwPO1mK1WxRiLSDbV/8l27hsLjK2XUJIkSZIkSUpFTUqxX5Ls\nu/VmCOFRYFbV+f2A4UApzqiSJEmSJElSI1DtUizGODOE8CXgeuD0qo/NXgZ+GGP8vKc8SpIkSZIk\nSQ1GTWaKEWOcBhwZQugM7AYEYG6M8aO6CCdJkiRJkiTVhRqVYptVlWAWYZIkSZIkSWqUstIOIEmS\nJEmSJNU3SzFJkiRJkiRlHEsxSZIkSZIkZRxLMUmSJEmSJGUcSzFJkiRJkiRlnFopxUIIebXxOpIk\nSZIkSVJ9qHYpFkIYGkK48jPnzg8hrAOKQgj3hxCa1XZASZIkSZIkqbbVZKbYT4G9Nx+EEPYBrgcW\nA88CZwAX1Go6SZIkSZIkqQ7UpBTbB5i2xfEZQAlwWIxxKPA34NxazCZJkiRJkiTViZqUYh2AFVsc\nHwc8F2NcV3X8PLBbLeWSJEmSJEmS6kxNSrEVQAFACKENcCjw0hbXmwHZtRdNkiRJkiRJqhs5Nbj3\nFeB7IYS3gaFVXztxi+t7AEtqMZskSZIkSZJUJ2pSiv0KmAw8VHV8d4xxFkAIIQAjqq5LkiRJkiRJ\nDVq1S7EY46yqJ04eCayNMb64xeX2wJ9I9hWTJEmSJEmSGrSazBQjxrgKmLCV86uB62srlCRJkiRJ\nklSXqr3RfgjhvRDCz0MI3eoykCRJkiRJklTXavL0yTLgamBBCGF8COGkEEJNvl6SJEmSJElqEKpd\nasUY9wMGAHcDg4BHgYUhhN+GEHavo3ySJEmSJElSravRTK8Y49QY42igOzAKmAf8AngvhPBcCOGs\nEEJeHeSUJEmSJEmSas0XWv4YYyyOMf41xjgQ2Bt4EDgGuBdYHEL4Uwghv/ZiSpIkSZIkSbXnC+8J\nFkLIDiGMAP4InAFEYDIwFbgIeCeEcEqtpJQkSZIkSZJqUY1LsRDC3iGEPwAfAv8ADgGuBfaKMR4X\nYzyRZPbYbOD3tRlWkiRJkiRJqg051b0xhPBt4DtA/6pTk4AxwKMxxvIt740xfhBCuAG4vbaCSpIk\nSZIkSbWl2qUYScG1FLgGuC3GOH87988i2WNMkiRJkiRJalBqUoqdDjwWY6yozs0xxteA175QKkmS\nJEmSJKkOVbsUizE+UpdBJEmSJEmSpPpSk5liAIQQupJsrt+BrWzUH2O8pxZySZIkSZIkSXWmJhvt\nZwE3AaPY9lMrLcUkSZIkSZLUoG2r3PqsnwDfBR4AzgUC8D/ABcD7wDTg+NoOKEmSJEmSJNW2mpRi\n5wJPxxi/ATxZde6NGOMtwMFAp6rPkiRJkiRJUoNWk1KsF5+UYZVVn5sBxBiLgL+SLK2UJEmSJEmS\nGrSalGIlQFnVeAMQgS5bXF8K9KylXJIkSZIkSVKdqUkpVgjsDhBjLAM+AIZscf04YFntRZMkSZIk\nSZLqRk1KseeAEVsc3wucGUKYHEJ4Hvgq8FAtZpMkSZIkSZLqRE4N7r0WeCaEkBdj3ARcTbJ8ciRQ\nAYwBflX7ESVJkiRJkqTaVe1SLMa4BFiyxXEF8IOqD0mSJEmSJKnRqMnySUmSJEmSJKlJqPZMsRDC\nAOBEYC+gLbAOmA08EWN8pW7iSZIkSZIkSbVvu6VYCKEt8ADJkybDVm75RQjhCeDsGOP6Ws4nSZIk\nSZIk1brqzBT7O3Ac8BJwBzCDZJZYW2B/YBRwEvA3YFjdxJQkSZIkSZJqzzZLsRDCCSSF2HUxxp9u\n5ZY3gbtDCNcCl4QQjo8xPlsHOSVJkiRJkqRas72N9s8ECoGfbee+nwELgLNqI5QkSZIkSZJUl7ZX\nih0MjI8xxm3dFGOsBMYDh9RWMEmSJEmSJKmubK8U25nkCZPVMRvYZcfiSJIkSZIkSXVve6VYW6C6\nT5RcD7TesTiSJEmSJElS3dteKZYFbHPpZA1fT5IkSZIkSUrdNp8+WWVYCKFbNe47eEfDSJIkSZIk\nSfWhOqXYWVT/qZI1mVUmSZIkSZIkpWJ7pdigekkhSZIkSZIk1aNtlmIxxhfqK4gkSZIkSZJUX9wY\nX5IkSZIkSRnHUkySJEmSJEkZx1JMkiRJkiRJGcdSTJIkSZIkSRnHUkySJEmSJEkZx1JMkiRJkiRJ\nGcdSTJIkSZIkSRnHUkySJEmSJEkZx1JMkiRJkiRJGcdSTJIkSZIkSRnHUkySJEmSJEkZx1JMkiRJ\nkiRJGcdSTJIkSZIkSRnHUkySJEmSJEkZx1JMkiRJkiRJGcdSTJIkSZIkSRnHUkySJEmSJEkZx1JM\nkiRJkiRJGcdSTJIkSZIkSRnHUkySJEmSJEkZx1JMkiRJkiRJGcdSTJIkSZIkSRnHUkySJEmSJEkZ\nx1JMkiRJkiRJGcdSrB6EylJ44y4oWZ12FEmSJEmSJGEpVi+yKkrhqV/AdXvDc7+BGNOOJEmSJEmS\nlNFy0g6QMcqKk8+v3JR8Hnx5elkkSZIkSZIynDPF6ltZMbx8I5SsSTuJJEmSJElSxrIUS0PIglnj\n004hSZIkSZKUsSzF0lBWAhuWp51CkiRJkiQpY1mKpaFZC2jdJe0UkiRJkiRJGctSLA2xEvY9Ne0U\nkiRJkiRJjU/JaqgopXkOeTvyMpZi9S1kw4CLoEX7tJNIkiRJkiQ1HjHCc7+B6/aG8o3kZdN8R17O\nUqy+ZOcmn2MFdOqdbhZJkiRJkqTGZvJv4ZWboHxjUpDtIEuxelCZnQtD/wC7H5ecmPhjWPthuqEk\nSZIkSZIai5LV8PKNUFZcay9pKVYPYlYuHPJNGHEztOwEG9fCo+dDZWXa0SRJkiRJkhq+WY9CqN0a\ny1KsPrXuAsOvT8Zzn4fXb0s1jiRJkiRJUqOwYTmUldTqS1qK1bd9ToIDRibjZ38JH72Xbh5JkiRJ\nkqSGrnUXyMmt1Ze0FEvDkKuhfX6yMdy40VBRlnYiSZIkSZKkhmvTOijfVKsvaSmWhuZt4dRbgABL\n/gMv/iHtRJIkSZIkSQ1PRRk88WN45oqqE6HWXtpSLC27HgkDLkzGL14Li6alm0eSJEmSJKkhKV4F\n946A129PjnufCAN+ADnNIex4OWYplqbBV0CX/SBWwLjzoLQo7USSJEmSJEnpW/4u3DYY5k9Jjo/+\nKZwxFr78a/jxu5DTnE0VbNyRb2EplqacPDjtVshqBqvmJBvvS5IkSZIkZbL3nobbj4PV85JZYaff\nAYMvh6yqGqtFB8jOZWM5O7TJmKVY2rr1hcGXJePXb4cPJqWbR5IkSZIkKQ0xwr+uh/vPgNL10KYH\nfOtJ6PuVOvl2lmINwYAfQP4RyXj8BcmaWUmSJEmSpExRthEe+V7VKroIOx8M502GnQ+qs29pKdYQ\nZGXDiFsgtzVsWApP/ChpRyVJkiRJkpq69cvg7pNgxoPJ8f5nwDcnQptudfptLcUaig67wpCrk/Hb\nj8DMv6caR5IkSZIkqc4t/g/cNggWvQ4EOO5KGHErNGte59/aUqwhOfAc2GtoMp74Y1j7Ybp5JEmS\nJEmS6spb4+DOIbDuw2T13JkPwMBLIIR6+faWYg1JCHDyDdCyE2xcC+O/D5WVaaeSJEmSJEmqPZWV\n8Nxv4e/fgvKSZPXcqEnQe2i9xrAUa2had0mKMYB5L8BrY9LNI0mSJEmSVFtKi+Dhb8CLv0+Odz0K\nRj0HXfap9yiWYg3R3ifCASOT8aRfwUez080jSZIkSZK0o9YsgDtOgHcmJMeHfBvOeQRadUwljqVY\nQzXkamifD+UbYdx5UFGWdiJJkiRJkqQvZsFUGDMIls2EkA3DroWT/gTZzVKLZCnWUDVvC6feAgRY\n8h944fdpJ5IkSZIkSaq5f98Ld50ExSugeftkdthho9NOZSnWoO16JAy4KBlPuQ4WTUs3jyRJkiRJ\nUnVVlMNTl8JjF0JlGXTqDaOfg15fSjsZYCnW8A2+HLrsB7EiWUZZWpR2IkmSJEmSpG0rWQP3fw2m\n3pQc7/llGPUsdNw93VxbsBRr6HLy4LQxkJ0Lq+bAs79MO5EkSZIkSdLnW/EB3H4czPlncjzgB3Dm\ng9C8Xbq5PsNSrDHo1gcGXZaMX78d3p+Ubh5JkiRJkqStmfMc3D4YVr6fTPA59Rb48v9BVnbayf6L\npVhjMeAiyB+QjB+9AIpXpZtHkiRJkiRpsxhh6i0w9iuwcS206gLffAIOODPtZJ/LUqyxyMqGETdD\nbmvYsBQevyT5AydJkiRJkpSm8lKY8EN46ufJnujd9ofzJkPPw9JOtk2WYo1Jh11hyDXJeNZ4mPlw\nqnEkSZIkSVKGK1oB95wC/747Od73VPj2U9Bul3RzVYOlWGNz4EjoPSwZP/ETWLso3TySJEmSJCkz\nLX0LxgyCBS8nx8dcCl+9C3JbpRqruizFGpsQYPgN0LITbFoL48+Hysq0U0mSJEmSpEzy7hNwx5dh\n7QJo1hK+dg8c8/Okt2gkLMUao9ad4eQbkvG8F+C1MenmkSRJkiRJmSFGePFaePAsKCuCtrvAt5+G\nfU9JO1mNWYo1VnufmCylBJj0K/hodrp5JEmSJElS01ZWAv8YBc/9X3Lc8/BkQ/3u+6eb6wuyFGvM\nTrga2udD+UYYdx5UlKWdSJIkSZIkNUXrFsNfh8Jbf0+ODxgJ506A1l3SzbUDLMUas+ZtYcStQIAl\n/4EXfp92IkmSJEmS1NQseiPZUH/xmxCy4ISr4JQ/Q05e2sl2iKVYY1cwAI78QTKeci0sfD3dPJIk\nSZIkqemY8XAyQ2zDUshrC2c9DEdc0Kg21P88lmJNwaDLoGsfiJXwyHlQWpR2IkmSJEmS1JhVVsKk\nK2HcKKjYBDv1glH/hD2PSztZrbEUawpy8pJllNm5sGouPHNF2okkSZIkSVJjtWl98nTJl/6UHPc6\nBkY/B533SjNVrbMUayq69UlmjAFMuwPefzbdPJIkSZIkqfFZNQ9uPx7eezI5Pvx7cPY/oEWHdHPV\nAUuxpmTARZA/IBk/egEUr0o3jyRJkiRJajzmvwS3DYaP3oGsHBh+PQz9HWTnpJ2sTliKNSVZ2TDi\nZshtDRuWweOXQIxpp5IkSZIkSQ3dtL/CPadAySpo2RG+8Rgc/M20U9WpjCzFQghXhhBiCOE/W7l2\nfAhhagihJISwPIRwawihfRo5v5AOu8KQa5LxrPEw8+FU40iSJEmSpAasogwm/hQevxgqy6HLvsn+\nYbsemXayOpdxpVgIYT/g58CyrVw7BpgILASGAz8BTgaeCCE0nv9WB46E3sOS8RV/0VIAACAASURB\nVBM/gbWL0s0jSZIkSZIanuJVMPZ0eG1Mctz7RPjOM8mEmwzQeIqeWlBVbN0B3A68u5Vbfg+8BZwR\nY5wUY7wH+AYwAPhqvQXdUSHA8BugZSfYtBbGfz95lKokSZIkSRLAR7Ph9mNh3gvJ8VE/hjPGQl6b\ndHPVo4wqxYBLgF2Ayz57IYSwM3AocG+M8eMGKcb4LPAhcHp9hawVrTvDyTcm43kvwmu3pptHkiRJ\nkiQ1DO8/C7cfB6vmQk5zOO12OPaXkJVZNVHG/LQhhF7Ar4ELY4zrtnJLn6rPb23l2swtrjceew9L\nllICTLoSlm9tcpwkSZIkScoIMcLLN8L9X4NN66B1N/jWRNi/8SyOq00hZsDTCUMIAZgErI0xnlZ1\n7nmgfYzxgKrjs4D7gENjjNM+8/X3AcfFGLtu5bXXbOfbt2vVqhVPPfXUjv8gX0B2eTEHvnohzUuW\nsaHN7kw/9DpiVrNUskjbsnbtWgDatWuXchKpYfO9IlWP7xWpenyvSNXTFN4robKMPd75M12XTAJg\nfdu9eKff5ZTmdUw52RczdOhQNmzYsDbG+IUfjpgpM8VGA4cAF1Xj3s9rCRtle1iR05L39v0RkUDr\n9XPoOe/BtCNJkiRJkqR61GzTKvq+8T8fF2LLux3DzIOvabSFWG3JSTtAXQshdCLZQP9qoCiEsLlB\nzAGyq443Aiurzm/tT8ROwKqtvf72GskQwpoQQruBAwd+kfi1ZCA0Xwz/up78+Q+RP3gU9Dw0xTzS\nf3vppZcASPe9IjV8vlek6vG9IlWP7xWpehr1e2XJdHjgu7BuERDg2F/SZeAldAkh7WQ7JDs7e4df\nIxNmiu0CtCMpxVZv8XEkyT5hq4Ergber7t/a3mF92fpeY43HoMugax+IlfDIeVBalHYiSZIkSZJU\nl94eD3cOSQqx3Nbw9fvhqB9BIy/EaksmlGIfAIO28jEdmFM1HhNjXARMA84OIXz83yWEcCywMzCu\nnnPXrpw8GHErZOcmT5d45vK0E0mSJEmSpLpQWQmTr4aHz4WyYmifD995Jnkgnz7W5JdPxhg3AM9/\n9vzmDfJjjFte+znwDPBACGEM0AP4HfAq8HBdZ61z3frA4Mvh2V/CtDuh9zDY8/i0U0mSJEmSpNpS\nWgSPfA/eeSw5LhgIX7sHWmX2/mFbkwkzxaotxvgccBKwK/AE8Meqz0NjjBUpRqs9R1wI+QOS8aMX\nQPFWt0qTJEmSJEmNzZqFcOcJnxRiB38TznnEQuxzZGwpFmM8JsZ4wFbOPxVjPDzG2DzG2DnGODrG\nuDqNjHUiKxtG3JysJd6wDB6/GGKjfLCmJEmSJEnabMGrcNsgWDoTQjYM/QOc9P8gJzftZA1WxpZi\nGa3DrjD0d8l41qMw46FU40iSJEmSpB3w5n1w90lQ9BE0bw8j/wGHn+eG+tthKZapDjgbep+YjCf+\nFNYuSjePJEmSJEmqmcoKePoyePR8qCiFTnvB6Odg90FpJ2sULMUyVQgw/Hpo1Rk2rYXx30+eTiFJ\nkiRJkhq+jWvh/jPglT8nx3scB6MmQcfd083ViFiKZbLWnWH4Dcl43ovw6i3p5pEkSZIkSdu3cg7c\nfhx88GxyfMSFcNZD0LxdurkaGUuxTLf3MDjwnGQ86UpY/m6qcSRJkiRJ0jbMfR5uGwwr3oPsXDjl\nL3DCb5MH66lGLMUEQ66G9gVQsQkeOQ/KS9NOJEmSJEmSthQjvDoG7j0NNq5JtkM693E48Oy0kzVa\nlmKCvDYw4hYgwJLp8MLv0k4kSZIkSZI2Ky+Fxy+GJ38KsQK69YXRkyH/8LSTNWqWYkoUDIAjf5iM\nX/ojLHwt3TySJEmSJAmKVsK9I+CNu5LjfU6Gbz8N7XumGqspsBTTJwZdCl37QKyER74LpUVpJ5Ik\nSZIkKXMtmwW3DYLCl5LjL/0PfPVuyG2Vbq4mwlJMn8jJg9PGJBv1rZoLz1yediJJkiRJkjLTuxPh\njuNhTSHktICv3gWDfgFZVjm1xf+S+rSu+8HgqjJs2p3w3jPp5pEkSZIkKZPECFOugwfPgtIN0HYX\n+M7TsN+ItJM1OZZi+m9HXAgFRybjxy5M1i9LkiRJkqS6VVYC40bDP38NRNjlMBj9HHTvl3ayJslS\nTP8tKxtOvRly28CGZfDEJUlTLUmSJEmS6sa6JfDXYTDz4eS431nwzcehTdd0czVhlmLaug4FMPSa\nZDzrUZjxULp5JEmSJElqqj78d7Kh/uJ/Q8iCL/8GTv1Lsve36oylmD7fAWdD7xOT8cSfwJqF6eaR\nJEmSJKmpmfl3+OtQWL8E8trCWQ/BgIsghLSTNXmWYvp8IcDw66FVZ9i0DsZ/Hyor004lSZIkSVLj\nV1mZ7B32j+9A+UbosBuMmgR7Hp92soxhKaZta90ZTr4xGc+fAq/ekm4eSZIkSZIau03r4W8jk6dM\nAuz2pWRD/c69082VYSzFtH29h8KB5yTjSVfC8ndSjSNJkiRJUqO1uhDuOAFmP5EcHzoaRv4DWu6U\nbq4MZCmm6hlyNbQvgIpNMO48KC9NO5EkSZIkSY3L/H8lG+ovfxuycuCkP8GJ10J2s7STZSRLMVVP\nXhsYcSsQYOkMeOF3aSeSJEmSJKnxeOMuuOdkKF4JLXaCc8bDId9OO1VGsxRT9RUcAUf+MBm/9EdY\n+Fq6eSRJkiRJaugqyuHJn8OEH0JlOXTeJ9k/bLej0k6W8SzFVDODLoWufSBWJssoN21IO5EkSZIk\nSQ1TyWq47yufPLRur6HwnWdgp93SzSXAUkw1lZMHp42B7FxYPQ+euTztRJIkSZIkNTwfvQe3HQtz\nJyfHAy+Br98Hzdumm0sfsxRTzXXdDwZfkYzf+Cu890y6eSRJkiRJakjenwS3Hwer5kB2Hpx2Gxx3\nJWRlp51MW7AU0xdzxAVQcGQyfvQCKFqZbh5JkiRJktIWI7xyE9z/Vdi0Flp3g289Cft/Le1k2gpL\nMX0xWdlw6s2Q2waKlsPjFydvfkmSJEmSMlH5Jnj0Qnj60mQf7h4HwnmTYZeD006mz2Eppi+uQwEM\n/V0yfucxmPG3dPNIkiRJkpSGDR/B3SfDf8Ymx31OT2aIte2Rbi5tk6WYdswBZ8HeJyXjiT+FNQvT\nzSNJkiRJUn1aMgNuGwQLpybHg6+A0++AZi3SzaXtshTTjgkBhl8PrTrDpnUw/vtQWZl2KkmSJEmS\n6t6sx+DOE2DtQmjWCs64D47+SfJvZTV4lmLaca06wck3JuP5U+DVm9PNI0mSJElSXYoRXvg9PHQO\nlBVDu3z4zjOwz0lpJ1MNWIqpdvQeCgd9IxlP+l9Y/k66eSRJkiRJqg0lq+n64VP0nPsAvHEXrF0M\nf/8WTP5tcj1/QLKhfrc+qcZUzeWkHUBNyAlXwdwXYE0hjBsNo56DnNy0U0mSJEmSVHMxJsXXyzfS\nK0ayKjbBgn9A+cVATO456Bsw7Dr/7dtIOVNMtSevDYy4FQiwdCa8cE3aiSRJkiRJ+mIm/xZeuQnK\nN5JdsYkAUF7Cx4VYr8Ew/AYLsUbMUky1q+AIGHhxMn7pT7Dg1XTzSJIkSZJUUyWr4eUbk/3CPs+C\nl2Hj2vrLpFpnKabad8yl0LUvxEp45LuwaUPaiSRJkiRJqr5Zj0LYTmUSsmDW+PrJozphKabal5ML\np42B7FxYPQ+euSztRJIkSZIkVd/6ZdueJQZQVgIbltdPHtUJSzHVja77wuArkvEbd8F7T6caR5Ik\nSZKkaln6Fvzn/u3f16wFtO5S93lUZyzFVHeOuAAKBibjRy+EopXp5pEkSZIk6fOUFsEzV8CtR8Oa\n+du/P1bCvqfWeSzVHUsx1Z2sbBhxM+S2gaLl8PgPk0faSpIkSZLUkMx+Em46HF6+AWIFdNwD+p4B\nzVpu/f5mLWHARdCiff3mVK2yFFPdap8PQ3+XjN+ZANMfTDePJEmSJEmbrV0ED54ND3wd1i6E7DwY\ndBl8/2U47dZkBVROcyqy84iEpAzLaZ6cH+T+2Y1dTtoBlAEOOAtmT4R3H4cnfwa7DoT2PdNOJUmS\nJEnKVBXl8OotMPkqKCtKzvU6Bk78I3Tc/ZP7Bl8OR1zA3MeuI3fTagr2OzRZMukMsSbBUkx1LwQY\nfj0sfC1ZRjn++/CNxyDLiYqSJEmSpHq2aBpMuBiWzUyOW3WBIVdDn9OTf79+VosOLNt5CAAFBw+s\nx6Cqa7YSqh+tOsHJNybj+VNg6l/SzSNJkiRJyiwla+DxS+D246oKsQCHjoILX4e+X9l6IaYmzVJM\n9af3EDjoG8n4n7+G5e+km0eSJEmS1PTFCDMehj8fCtPuBCJ06wujJsGJ17kUMoNZiql+nXAVdNgV\nKjbBuNFQXpp2IkmSJElSU7VyDtx7KowblWzn06xV8u/S0c/DLoeknU4psxRT/cprAyNuhZAFS2fC\nC9eknUiSJEmS1NSUb4Lnr4G/HAFzn0/O7TMcLnwteXJktlusy1JMacjvD0f+MBm/9CdY8Gq6eSRJ\nkiRJTcfcF+DmAfD81ckqpXY94cy/wRljod0uaadTA2IppnQccyl07QuxEh45DzZtSDuRJEmSJKkx\n27Acxp0H95wMKz+ArJxkQsYFryZ7XEufYSmmdOTkwmljIDsXVs+HZy5LO5EkSZIkqTGqrIRpf4U/\nHwIz/pac63k4fPdFOP7XkNsq3XxqsCzFlJ6u+8Kxv0zGb9wFs59KNY4kSZIkqZFZ+hbceQI8fjFs\nXAvN28PwG+BbT0HX/dJOpwbOUkzp6n8BFAxMxo9dBEUr0s0jSZIkSWr4Nm2AZy6HW4+GRa8l5/qd\nCRdOg4PPhSzrDm2ff0qUrqwsGHEz5LZJHo/7+MUQY9qpJEmSJEkN1bsT4abD4eUbIVZAxz3h3Akw\n4hZo3TntdGpELMWUvvb5MOz3yfidCTD9wXTzSJIkSZIanjUL4YGz4MEzYd0iyM6DQZfB9/8Fux2d\ndjo1QpZiahj6nQl7n5SMJ/4U1ixIN48kSZIkqWGoKEtmhd10OMx+IjnXaxCc/wp86WeQk5duPjVa\nlmJqGEKA4ddDqy5Quh7Gn588QUSSJEmSlLkWvg5jjkn2DysrSv7NePodcM4j0HH3tNOpkbMUU8PR\nqhOcfGMynj8Fpv4l3TySJEmSpHSUrIYJF8Mdx8Oyt4AAh46CC1+Hvl9JJlZIO8hSTA1L7yFw0LnJ\n+J//C8tmpZtHkiRJklR/YoQZD8GfD4U3/gpE6NYXRv0TTrwOWrRPO6GaEEsxNTwnXAUddoWKUhh3\nHpSXpp1IkiRJklTXVnwA95wC40ZD0UeQ2xpOuBpGPw+7HJx2OjVBlmJqePJaw4hbIWTBspnw/NVp\nJ5IkSZIk1ZWyjfD8NXDzETDvheTcPsPhgtfgiPMhOyfdfGqyLMXUMOX3hyMvTsb/+n+wYGq6eSRJ\nkiRJtW/OZLh5QDIZoqIU2uXDmX+DM8ZCu53TTqcmzlJMDdcxv0jWjsdKeOS7sGl92okkSZIkSbVh\nw3L4x2i491RYNQeycpKJERdMTfaaluqBpZgarpxcGDEGsnNh9Xx4+rK0E0mSJEmSdkRlJUy7E/58\nCMx8KDnXsz9890U4/n8ht1W6+ZRRLMXUsHXdF479VTL+990w+6l080iSJEmSvpilM+HOL8Pjl8DG\ntdCiA5x8I3zrSei6X9rplIEsxdTw9T8fdj0qGT92ERStSDePJEmSJKn6Nm1IVv7c+iVY9Hpyrt9Z\ncOE0OOgbkGU1oXT4J08NX1YWnPoXyGsLRcthwg8hxrRTSZIkSZK2590n4KbD4ZU/Q6yAjnvCuY/D\niJuhVae00ynDWYqpcWifD0N/l4zffRymP5BuHkmSJEnS51uzEB44Ex48C9Ytguw8GHQ5fP9fsNtR\naaeTAEsxNSb9zoR9hifjiT+D1YXp5pEkSZIkfVpFGfzrBrjpMJg9MTm3+2A4/xX40k8hJy/dfNIW\nLMXUeIQAJ10PrbpA6XoYf37y5BJJkiRJUvoWvgZjjoFnr4CyYmjdFb5yJ4wcBx13Tzud9F8sxdS4\ntOoIp/w5GRe+BFNvSjePJEmSJGW6ktXJ3s93HA/L3gICHDoaLnwd+pyeTHCQGiBLMTU+e50AB52b\njP/5a1g2K908kiRJkpSJYoTpf4M/Hwpv3JWc67Y/jP4nnHgtNG+XajxpeyzF1DidcBV02BUqSmHc\neVC+Ke1EkiRJkpQ5VrwP95wMj5wHRR9BbmsYcg2Mngw7H5x2OqlaLMXUOOW1hhFjIGTBspnw/NVp\nJ5IkSZKkpq9sI0y+Cm4eAPNeTM7tc3KyVLL/9yE7J918Ug34p1WNV/7hcOTF8NIf4V/Xw15DIL9/\n2qkkSZL0WSWrYdajsGE5tO4C+54CLTqknUpSTc2ZDE/8GFbNSY7b58Owa5MtbqRGyFJMjdsxv4AP\nnoWlM+GR78L3XoK8NmmnkiRJEiT7DU3+Lbx8YzLDv6wEmrWAJ38OAy6CQZe5AbfUGKxfBs9cBjMf\nTo6zcpL38NE/g9yW6WaTdoDLJ9W45eTCabdBdh6sng9PX5p2IkmSJG02+bfwyk1QvhHKioGYfC7f\nmJyf/Nu0E0ralspKeP2OZCP9zYVYz/7w3Slw3JUWYmr0LMXU+HXZB479ZTL+9z0w+8l08/z/9u48\nvu6qzv/466RJ2nRvgdLSAgXK0oUd1ALKjiBVNkcdHQV1ZlwYHB1HUHF+OiqOOuqouM6MgorjOFpk\np6CyKKssZWkLtFCg0BYKdG/TNMv5/fG5ITfpbZuUJDfJfT0fj/O4zff7vd+eFL53eX8/5xxJkiTF\nkMm7Li2EYSU0boz99at7t1+SOueFR+EnJ8P1/wQNa2LI89suhfffCLtOK3fvpG7h8EkNDG/4KCyc\nA8/8Ga65AD56Dwzbudy9kiRJ6v9yhs3rI7yqXwWbCo/1q7axbTVsWBEVYduSqmDBVXD4eb3yq0jq\nhIb1sZDZPT+E3BzbDn43nPIlv2NpwDEU08BQVQVn/gB+eHQsB3ztP8I7r3COCkmSpFbNjSVCrKIw\na1vbWpp6pk+NG2H5wz1zbkld99h1cOOFsHZp/LzzfnD6t2CvN5a3X1IPMRTTwDF6Dzjt63DVh+Hx\n6+Ch/4FD31PuXkmSJHWfnKFhXReCrdVt2zav76ZOJBgyMoZS1Y2BIaMLfx7dftuyufDQFdDUsO3T\n3f9TWP4IHPlBmH5WTMQvqXetXhILYDxxQ/xcPQTe9M9w1D/GPM7SAGUopoHl4HfBE9fDY9fGi/rk\nY2DMnuXulSRJ6k31q9h16RxqG1ZB3ZMw7YwIavqSps07VrFVv7ptONNrVT1kG8HW6K2HXUNGQdWg\n7Z9/6ix46JfbOSgBGZbeH+2mz8Ih74EjPgA77dMdv6WkbWluhHt+ALd9tW3+v31OhNO/AWP3Lm/f\npF5gKKaBJSWY9R1Ycm/MY3HVR+Dcazv3wU2SJPVvOcdqhnddyt45U9XcAM9dGTfKjroAjr+4e6dW\naGmBzes6Mb9Wh4qt+tXQuKGbOpE6BFhFIdY2t43u+YqsujHx737390tPtl8zNOaF3ecEuP8nsOCa\n+Pe5+3vR9jkxqsf2fTMM8muL1O2W3AvXfQJWzI+fh+8Kp341KjadhkYVwncXDTzDdoIzvgf/8w54\n9s74IHb0x8rdK0mS1NNuvSTe95s28ertsNYw5u7vx+MJn9vyeY2btqzO6sxk8ptWQ27pnr7XDC0R\nYnUi7Bo8MuZW7auOvzge77o0JtVvrI8wLrfAzPPbgsrJR8P6FbGS+P2Xwdrn4ak/Rhs5KSbiP+x9\nMGLXsv460oCwcSX88V/hgcsLGxK87u/i9XHIqHL2TOp1hmIamPZ7c3x4euByuOVLMOVE2HV6uXsl\nSZJ6Sv2qCF62ttph40b487di7qrN69uHXU313dOHVLX1IYfbq+KqHtw9fehrUoov2jPPhwVXR/A1\nfBxMOzN+72LDx8UcRsd8AhbeFNVjT/4hArJbvwy3fxWmvi2qx/Y82koWqatyhkd+DTddDBtfjm0T\nDoZZ34aJh5W3b1KZGIpp4DrlElh8O6x6Gq78EPzdHwfuB05Jkird/Ku3f0xuhkU3bf+4mmGdn1+r\neFvtiL5dtVVOdWPihmVnVA2CA94SbeXiqByb+4sIMOdfGW2XA+CID8LB77SyReqMlxfFUMln/hw/\n146IwPrIv3V4siqa//dr4Bo8HM76MVx2Krz4KNz2b3DSF8rdK0mS1F1yhhcehXmzIzjZWpVYsYlH\nRAX5tiq4XGmt7xi7N5zyJTj+szD/qqgee/4+eOlxuPFT8IcvwEHviOqx8QeWu7dS39O4Ce74Ftzx\nH9C8ObZNOyPmDhu5W3n7JvUBhmIa2PZ4fZTg//mbcMe3Y6LWPWeWu1eSJOm1eGlhBGHzZsMrizr/\nvJqhcNh7O1+xpL6jpg4O+etoyx+G+34Cj/4mFix44LJou78+ql6mneHoAAngqVvg+k9GxSXA6D3g\nLd+E/U4pb7+kPsRQTAPfsZ+GRb+HFx6B330Izrs+Jm19dU6LPrhMuyRJam/VMzDvymgvPtp+305T\nYP/T4d4ftlVClJJbYi4r9W8TDoa3fRdO/iI8/L9RPfbyQnju3mhzPg2HvheOeD+MmVzu3kq9b92L\ncNNnYd5v4+eqajjqY/CmT0Ht0PL2TepjDMU08FXXwtn/CT96E6x+Fr5zcNw9bF39qKeWaZckSa/N\n2mUxZG7ebFh6f/t9o3aHGWfDjHNg/EHxHl5dG6tMtq44WaxmaEz23nFyd/VfdaPhDR+G138o5km6\n77/h8eth4ytw57fhzu/AvidH9diUk2KuMmkga2mOqsk/fBEa1sS2PWbCrP+AcVPL2zepjzIUU2UY\nNzWW+l58a0yy2/phuTPLtEuSpN6z4eVYpXDelfDsnUBu2zd8V5h+VgRhk47c8mbW8RfH412X0pwz\nVc2bSTV1USE28/y2/RpYUoK93hRt7XJ48OexAvm6ZbDo5mij9ojKsUPfC8N3KXePpe63/BG47uOw\n9IH4uW4MnPwlOOQ9LgAibYOhmCpD/SpYcvfW9zdujGXcZ/6Dd5AlSept9aujwmfebFh8W9zAalU3\nJqY6mHEO7Hn0tqt9UoobXDPPZ/E136S2YRV7Tj8yhkz6/l4ZRk6A4y6CN34SFt4Y1WOLb4M1S+CP\n/xoLL007I6rHdn+9owTU/zWsg1v/LYaP55bYdsh7IhAbtlN5+yb1A4ZiqgwLroa0nTskqQoWXOXk\nu5Ik9YbNG+CJG6Mi7Mnft58LrHYETJ0VQdjex8Ggmq6du24ML048FYA9Dz+m27qsfmRQNUx9a7SX\nn4T7fwoPXQGb1sQE/Y/+BsZNj1UrD3oHDB5R7h5LXZMzPH5dTAWzdmls23l/mPUtmOzrntRZhmKq\nDOtXxBxi29K4ERbfDge9M+YakyRJ3atxEzz5h6gIWzin/dxf1XWw/6kRhE05GWqGlK+fGlh2ngKn\nfiWqCOdfGdVjy+bCivlw/T/B7z8PB78rAjLnXVJ/sHoJ3PCpeB0FqB4Sk+gf9bGYW1FSpxmKqTIM\nHxdBV6mJd4vNvzLmndj/LfGhfJ8TfGORJOm1aG6Mm07zZkdVQ8Patn1VNTER+oxzYL9TYfDw8vVT\nA1/tUDj0b6ItfQDu+2mszrd5Hdz3X9H2PBqO+ABMfZufAdX3NDfGXMi3f63te82Uk+At34Cxe5W3\nb1I/ZSimyjDtjCgt3pZUBSTYvB4e/b9oQ0ZH2f2Mc2DyG6MUX5IkbVtLMzx7VwRhC66G+pVt+9Ig\n2PvYeG894PSYM0zqbRMPj3bKl+DhX8F9P4GVT8XiDs/eCcN2gcPOjWk1Ru9e7t6qktSvitfN9Svi\nxv60M+J1csk9cN0nYMWCOG74eDjtqzFnonPjSTvMb/iqDHVj4KgLtr9M+xs+Co9dEx/in/4zbFoN\nc38Rbdgu8aYz45yYmNVVXCRJapNzVN/Mmw3zfwfrlrffv+fRMONsmHqGq/+p7xg6Nj4Dvv4j8PRt\nEY49cQNseAn+/A2441tRxXjEB2MEgZ//1FNyhlsvicW/UlVM/VJTBzdcCLvsBy88Wjgwwev+Hk64\nGIaMKmuXpYHAUEyVo2iZ9nZvNMXLtKcUdwQPPw/WvVBYEn42PHdvfDhqLa0fObFtSfjdDvXujCSp\nMuUcX9TmzY4pCFYvab9/4uHxXjntTBg1sTx9lDqjqipCr31OgDVL4cGfwQOXw/oXIyR74gYYs1cM\nrTz0byJMk7rTrZfEDfymTW3bWm/mtwZiEw6GWd+GiYf1fv+kAcpQTJWjaJn29iXJW1mmfcR4eP2H\noq1eEqtjzZsNLzwSK7zc/b1oY/aKD/wzzoFdp/X+7yVJUm97aWG8J86bDa8sar9v1xlRETb9bOe4\nUf80aiIc/9mYuPzx66J67Jk/w6qn4ff/Ard8Of4fP+KDMOkIb47qtatfFTfuiwOxjqqq4b1XGchK\n3cxQTJWnbkxUgnXF6D3gmI9He3lRISD7Lby8MD4g/fkb0XaZWgjIzoad9umR7kuSVBarno1qsHmz\ni4bxFIzdBw58ewRh4w4oT/+k7jaoJkYGTD8LXnoC7v8pPPQ/sVjEw7+KNv4gOPJv4///2mHl7rH6\nm+ZGeHF+VIi1NG372EG1Mc1LV7/HSNomQzGpq3beF467CI69MN7EWu+Ur34WXnoMbv1ytAmHFL4g\nnAWjJpW715Ikdd3a5bDgqnife/6+9vtG7R43gWacE8GA1TIayHbZH077Gpz4/+DR38B9/x3h8AuP\nwLUfg5v/BQ7566ge22W/cvdWfVFLc9xcX/YgLH0wHl+YB80NnXt+Y32MdJHUrQzFpB2VEoyfEe3E\n/xdvbq1zqqxbDssfinbz52CPmYU5Vc6IIZuSJPVVG16Bx66Oquhn7gBy277hu7bNqTnxCCcdV+Wp\nHRaVOoedC8/fH+HY/N9Bwxq490fRJr8xqscOOD2qzVR5coZVzxQFYHNhQTAa9QAAIABJREFU+cOx\nyn0pg0fA5o2Qm7d+zpo6v0dIPcBQTOoOKcGkw6Od8mVYcndhGfqrYOMr8fOSu+HGC2GvNxWWoZ/l\nnACSpL5h0xp4/Pp473rq1vZfzOrGxE2dGefECpJVg8rXT6mvSAl2PzLam78CD10RwytXPRPzjz3z\nZxg+Hg4/NwI0F5oY2NYubx+ALZsL9StLHztkdCzUNfEw2O2weKypg28eAE3bCMVyS8yFLKlbGYpJ\n3a2qCiYfHe20r8PTt8fd9seujbuIi2+Ldt0/wZQT40vG/qfFHSJJknrL5g2wcE68Ry26GZo3t+2r\nHQFTZ8V71N7HWe0ibcuwneDof4SZF8BTt0T12KKbYP0LcPvX4E/fiM96R/4t7HWsFZb93caVEYAt\nmwtL58af1y0vfWzNsFgxcuJhbUHYmL1KDzc/6oKYW6x1xcl25xkai4WVWhxM0mtiKCb1pEHVEXxN\nORFmfQue/GPchX/ihnjDWzgnWvUQ2O/N8eVj31PibpEkSd2tqQGe/EPhvejG9l++quvivejAt8OU\nk6FmSPn6KfVHVVWw70nRVi+BBy6HB38OG16KVSwfvw52mgJHfAAOeXdUYapva1gXwx6XzW2bB2zV\nM6WPHVQbq+8WV4DtvF/nq2uPvzge77oUUlXMIVZTFxViM89v2y+pWxmKSb2lejAc8JZomzfAwpvi\nS8mi38fyywuujlY7POagmHEO7H08VNeWu+eSpP6subGoavm6qFpuVVUDU04qVC2fatWy1F1G7xFz\nzh776Vgx8L6fwJK74JUn4abPwh+/FNfdkR+M8ETl17gJXpzXPgB76QnazavYKlXFqvMTD20LwMZN\nf22f21OCEz4XAdiCq2NS/eHjYsikFWJSjzEUk8qhdlhhxa6zC/O43BAB2eJbYwLOR34dbchomPa2\n+NA0+Y3O4yJJ6pyW5qL5La+O+S1bpaoYwjXjnBgiabWK1HOqa6P68sC3w4sL4P6fwMO/hs3rYh6y\nh66IUOXID8L0s6F2aLl7XBmam+Clx9uvBPniAmhpLH382H3aV4CNPzA+z/eEujGxmIOkXmEoJpXb\nkFGxhPchf11Y8eua+BLzzB2waXWU3T/4cxg2DqafGV9iJr3O+SgkSe3lDEsfKKyE/Lst57jZ46i4\nGTPtTBi+S3n6KFWyXafB6d+Ek74Aj/xfVI+tmB+BzNUPwk0Xw6F/E8Mrd9qn3L0dOFpaYOXi9gHY\n8kegqb708SMnta8Am3CIlVrSAGYoJvUlw3aCI94fbe3yuLs/bzY8/xfYsAL+8p/RRk6CGWdFQDbh\nkNKTdUqSBr6cY7jPvNnRVi9pv3+3w+K9YvpZrn4n9RWDR0Rl2BEfgCX3RPXY/KviZujd34u29/Fx\nzH6nxRy16pycYc3z7QOwZQ+3HzZebOjO7SvAdjs0hixKqhi+wkp91cgJ8IYPR1v1bNz1nzcbXngE\n1j4fk3DedSmM3Tu+8Mw4B8ZNLXevJUm94eVFbUHYywvb7xs3vW2I/ti9y9M/SduXEuw5M9qb/w3m\n/hzuvxzWLIkpNRbfCiN2i5ulh70PRowvd4/7nvUvdQjA5sbCBqUMHgm7HdI+ABu1uzeXpQpnKCb1\nB2P2hGM+Hu3lRTFZ8rzfxhehlYvhT/8ebdy0+BI0/WzL7iVpoFn1LMy/snCD5NH2+8buU7hBcrY3\nSKT+aPgu8MZPwtEfj0WY7v9JPK5bBrdeArd/DQ6YBUf+LUw+pjKDnPrVsPyhtgBs6dy4UVxKdR1M\nOKh9ADZ2H6cfkbQFQzGpv9l5XzjuIjj2QnhxftGQmWdhxQK4ZQHc8uV48391yMykcvdakrQj1i6H\nBVcVhtLf137fyEmFirBzYMLBlfklWRpoqgbFSrD7nworn4YHLoO5V8RiGQuuirbz/jG08uB3xdy0\nA9HmjTE64tUA7EFY+VTpY6uqYdfpEYDtdmiEYLtMddippE7xlULqr1KC8TOinfj/4sPCvNlRRbBu\neZSPL5sLN38O9pgZX5qmneE8CZLU1214BR67OqqCn7kDyG37ho2Lmx0zzoFJR1r1IA1kY/eCk78I\nx3025pm9/yfw3L3w8hNw44Xwhy/AgX8V1WMTDip3b3dc0+ZYcODVIZAPwYrHIDeXODjBLvu3D8B2\nnQE1Q3q925IGBkMxaSBICSYdHu2UL8OSuyMgW3BV3Flccne0Gy+Evd4UX6amvjWWfJYkld+mNfD4\n9fHa/dSt7b8M1o2BqW+L1+7Jx0QliaTKUTMEDn5ntOWPRDj2yG+gcQM8+LNok46McGzamX07IGpp\njuk/lhbm/1r2ILwwD5obSh8/ZnL7AGzCwbFQgSR1E0MxaaCpqoLJR0c77evw9O3xJeuxa6FhLSy+\nLdp1/wRTTowvWfuf5gcMSeptmzfAwjlREbboZmje3LavdgQccHq8Ru99HFTXlquXkvqSCQfBW78T\nFWQP/xru+++oHHv+vmhzPgOH/k2sbDl2r/L2NWdY9XRRADYXlj8Mm9eXPn7EhKIA7ND489Cxvdtn\nSRXHUEwayAZVR/A15USY9R/w5B8iIHviRmjcGF/GFs6B6iGw35vjy9e+p0BNXbl7LkkDU1ND4bX4\nysJr8Ya2fdVDYL9TC6/FJ/taLGnrhoyC1/89vO7v4Nk7Ixx77FqoXwl3fTdWKJ9yYlSP7XtK71SY\nrl0WwVfxSpD1q0ofWzcmwq9XJ8I/LFZel6ReZigmVYrqwVF1cMDpW1YnNG2KuSoWXA21w4uqE463\nOkGStqV+Vbx2rl8RczZOO2PLoenNTYWq3SsLVbtr2vZV1cCUkwpVu6datSupa1KKYdWTj4F1L8KD\nP4/J+dcujQD+yT/AqN3h8PPgsPdtObds/Sp2XTqH2oZVUPdk6dewUjaubFsBsnUi/PUvlD62Zhjs\ndkjbEMjdDothkS4OIqkPSDnn7R+lHZZSWj18+PBR69atK3dXpNL6yDw2d9xxBwDHHHNMj/0d0kDg\ntdJH5Ay3XhLVGKkKGuujsiu3wFEXwLGfgefuaT+/Y6tUBXsdW5jfcZbzO/YQrxVVrOYmWHRTVI89\ndUvb9qqaCL2O/CDs/ga47Stw16U050xVcwOpZmjba9jxF7eFVg3rYthj8UqQq58t/XcPqoXxB7av\nANt5X+dC1IDg+0rfM3r0aNasWbMm5zx6R89hpZhU6YaMgkPeHa3jimf1q9omcHXFM0lqc+slcPf3\no9K2VePGeLzjP+CeH0RVbrE9joIZZ7sSsKSeNai6bXTAK0/B/T+FuVfAptUw77fRhu4cN0ZbGnk1\nrmp9DbvruxF8DR8Xjy8vpN0quK3SIBg3tX0F2LhpjjKQ1K8YiklqM2ynmJj1iA/A2uVR3TBvdkzc\numEF/OXH0Ubt3haQTTjY8ndJlaV+VVSIFQdixVqaYHNT/Hm3w+K1cvqZMGpS7/VRkgB22gfefAmc\n8DmY/7uoHlv6AGx8eevPaWqAp/5Y4lxT2leAjT8Qaof2XN8lqRcYikkqbeQEeMNHoq16Jj5IzZsN\nLzwKa54rTOL6XRi7T3zhm3EOjDug3L2WpJ7TWA/LH4F7fwQtjds+tqoGjvs0vOmfe6dvkrQtNXVt\nIwP++CW489sR4G9VgvEHwYyzIgCbcDDU7fDoJEnqswzFJG3fmMlwzCeivbQQ5l8Jj/4WXlkEK5+C\nP3092rjpMTRoxtkwdu9y91qSdlxzI7w4v20FtaVzYcWC9vMubktLU8zNI0l9TfVgaOnEa9nUWfHZ\nT5IGMEMxSV2zy35R/XDsRfDivKgemzcbVi+BFfPhlvlwy5eKhgydBaMmlrvXkrR1Lc3w8qKiAOzB\nqIptbih9fN3YmItnWwFZTZ3zhknqm4aPi9eo1jnESvE1TFKFMBSTtGNSirkkxh8IJ34+5qeYNzsm\n6V//QuHL5YNw88VFk0ufCcN32fJcO7ocuCR1Vc4xJLy4Amz5Q7B5fenjh49vmz9n4qEw4dBYaOSb\nB0DTNkKx3BKveZLU10w7A268aNvH+BomqUIYikl67VKCSUdEO+XLsOTuCMjmXwX1K2HJXdFuvBD2\nOjYqyKbOgiGjYwW3uy5l78Jy4Dx3ZXxQ67gcuCTtiLXLI/xa9mBUgC2bG69LpQwZ3RaAta6mNnK3\n0scedUGsPlmq0qJmKMw83/l3JPVNdWN8DZOkAkMxSd2rahBMPibaaV+Hp2+P6rHHroWGtbD41mjX\nfQJG7xGT9jdv3nI58Lu/H48nfK4cv4Wk/mjjyqIArPC4bnnpY2uGwW6HRPjVGoCN2avzQfzxF8fj\nXZdCqopJ+Gvqorpi5vlt+yWpLyp6DWvOmarmzSRfwyRVIEMxST1nUA1MOSna6d+K5b3nzYYnbozw\na+VTW39u40a487tw2Ptg1O5WjElqr2E9LH+4qALswRgWWcqg2hjqXVwBtvN+EeLvqJQitJ95Piy4\nGtaviPl3pp1pdYWkvq/oNWzxNd+ktmEVe04/0tcwSRXHUExS76gZAgecHm3zBpjzGZh7xbYnqm5u\ngG8fCGlQfEAbMjpK/uvGxM91Y7azbXSssCSpf2vc1LYSZGsA9tITQN7y2DQIxk1tXwE2bjpU1/ZM\n3+rGwOHn9cy5Jamn1Y3hxYmnArDn4ceUuTOS1PsMxST1vtphMGpSlOh3Rm6Gja9E66qaoVsGZXVF\nQVq77UXbBo+MybQl9a7mJnjp8fYB2IsLoKWx9PE7TSkEYIdFADb+IKgd2rt9liRJUr9UEaFYSulE\n4L3ATGB3YCXwF+DzOedHOxx7MvAl4GBgHfA74KKc8+pe7bQ00HVmOfDquhiaNPloqF8N9auibWr9\n8+q27a3bOp6vcWO0tUu71r9UBUNGbT88K1WlVjOk6/8eUiVqaYGVi9sHYMsfgab60seP2r0wD1gh\nAJtwiMN8JEmStMMqIhQDPgzsBPwH8BiwK3AhcF9K6bic8z0AKaXjgBuAq4DPAbsBXwNmpJTemHNn\ny1okbVdnlgMnx+pIXfnS29SwZVC21UCtw7biSzy3tO3vquq6rQ/n3Nawz8Gj+k51Wv2qDvMknRF9\nlHZUzrDm+fYB2LKHoWFN6eOH7ty2EuTEwlxgw8f1bp8lSZI0oFVKKHZ+znlF8YaU0s3A08CngHMK\nm78OzAPe2RqApZSWAzcDfwX8utd6LA10PbUcePVgGLFrtK7IGRrWdS4861ihtnl9+3M11cO6eli3\nrGt9IBVVp3ViiGfxtpq6Lv5d2/h3uPWSLVfUu/Gi+O91/MUueqDOWf9ShwBsLmx4qfSxg0e2rwDb\n7bAYYu3/a5IkSepBFRGKdQzECttWp5QWAZMAUkoTgSOBTxZXhOWcf59SWkoEZ4ZiUnfqS8uBpwRD\nRkYbs2fXntu0uRCYdaxQW7X9bS1NRSfKsW3TauhqgVr1kM6FZ3WjYUhxFduo9ivw3XpJBJVNm9q2\ntYaWd38/Hk/4XBc7pwFv05oIvV4NwB6CNc+VPra6DiYc1D4AG7t336mSlCRJUsWoiFCslJTSLsAM\n4FeFTTMKj/NKHP5o0X5J3WWgLAdeXRvDuro6tCvnqDLrTHj26rY18bh5XftzNW2C9S9E66ohowqL\nC4yAFQu2vgBC40a48zsw/ZwIDmvqrOSpRJs3wguPFAKwuRGCvfJk6WOrqmHX6e0DsF0OgEEV+/FD\nkiRJfUjKucRy5gNcSikBVwKnAYfmnB9LKb0b+CVwZM75/g7H/xI4Kee8xXislNL2JuAfNWzYMObM\nmdNNvZcGpjVrYl6hUaNGlbkn/UNqaWJQ0wZqGtdR3bSe6sZ1VDcWHpvWU924vrBvy+1VuWn7f0En\ntKRqmmpG0FQznMbqeGyqGUFT9fC2Pxd+bqwZTlPrMdXD21enqUt681pJLY0MXf8MI9YuYvjaRYxY\nu4ihG54llQhOM4mNw3Zn/ch9WT9yP9aNnMKG4XuTB9X2eD+lUnxfkTrHa0XqHK+Vvue0005j/fr1\na3LOO1xRUam3av8dOBN4f875sQ77tpYSVl56KKnPylXVNNWOoqm2i2/KOVPVvInqpkJo1rj+1eBs\n5xfvYPTKB+ls7VdVbqJ28ypqN3d9MYKmQUNfDdRKh2ptAVpx8NYyaIjVaT0lNzN0w/MMX7uQ4Wuf\nZMTahQxb/zRVLY0lD6+vG8/6kfuxfuS+rBu5LxtG7ENz9dBe7rQkSZK04youFEspXQJ8EvjHnPPl\nRbteKTzuVOJpY4GVpc63vUQypbQ6pTTqmGOO2YHeSpXjjjvuAMBrpYweuBzmPF564YNWNXVwzCdh\nn+M7v6pn/Spo3tzuNNXNG6lu3gibXuxaH6tqur4IQevcaYNquv5v0gd1y7WSM6x6umgI5FxY/vCW\ni0a0GjGhMATy0FgFcrfDqBs6ljpglx3vhdSjfF+ROsdrReocr5W+Z9Cg1z76pKJCsZTSF4HPAhfm\nnL/bYff8wuMMYrXJYgcCd/Vw9ySpvKadEatMbkvO8Lq/69qcbznHKpadWtWzw7ZNa2lXqNvSGCsY\nbm0Vw22pHVEIykZ3LVCrHd53qtPqV7Hr0jnUNqyCuifjv1ndmO0/b+2y9qtALpsb/76l1I2JAGy3\nQ9vmARs5oXt/D0mSJKkPqJhQLKX0eeBfgH/JOf97x/055+dTSvcD70kpfbt1BcqU0onARGIOMkka\nuOrGwFEXxCqTparFaobGqqBdXQQhJagdGm3UxK49t6U5VjYsGZ6t3vrCBBtXQnND+3NtXhdta6si\nbk1VdSEs62KF2pDRsQhDd8g5Vga961L2zpmq5gZ47soIMY+6IFZqbQ3uNq5sPwn+0ge3vgBD7XCY\ncAjsdkhbADZmct8JASVJkqQeVBGhWErpk8AXgOuAP6SU3lC0uyHnPLfw54uIKrFfpZT+E9gN+Bpw\nL/Cb3uuxJJXJ8RfH412XQqqKCq+auliRcub5bft7S9UgGDo2Wlc11ndhVc/ibatpX53WBBtfjtZV\nNcM6hGejOxeoDR7ZPpi69ZIIK5s28WqReGtweed34bm/xL/R0gdh9bOl+zJoMIw/sH0F2M77uuiB\nJEmSKlZFhGLAWwuPswqt2LPAZICc8y0ppVnAvwLXA+uAq4jhls2901VJKqOU4ITPRQC24GpYvwKG\nj4NpZ3a9QqzcauqidXXoX0sLNKzpXHjWcVvHCrvGDdHWPt+1PqRBMQ9a3RgYPBxeeDSCyVKaG+Dp\n27d8/rhp7SvAxk3rvso1SZIkaQCoiFAs53xcF46dA8zpud5IUj9QNwYOP6/cvSiPqqq2Ci726tpz\nGze1D8y6UqFWHHrlZqhfGa1TUoRfB/5VBGDjD4zhqpIkSZK2qiJCMUmSekXNEKgZDyPGd+15LS0x\n31mpBQcWXA2Lb9v+OfY7Fd7wkR3qtiRJklSJDMUkSSq3qqoYLjlkFHRcTDJVxZxhpRY/aFVTF8Nc\nJUmSJHVaVbk7IEmStmHaGVufT6xVbol53yRJkiR1mqGYJEl9Wd0YOOoCqNnKHGE1Q2N/f1sIQZIk\nSSozh09KktTXHX9xPN51Kc05U9W8mVRTFxViM89v2y9JkiSp0wzFJEnq61KCEz4HM89n8TXfpLZh\nFXtOPzKGTFohJkmSJO0QQzFJkvqLujG8OPFUAPY8/Jgyd0aSJEnq35xTTJIkSZIkSRXHUEySJEmS\nJEkVx1BMkiRJkiRJFcdQTJIkSZIkSRXHUEySJEmSJEkVx1BMkiRJkiRJFcdQTJIkSZIkSRXHUEyS\nJEmSJEkVx1BMkiRJkiRJFcdQTJIkSZIkSRXHUEySJEmSJEkVx1BMkiRJkiRJFcdQTJIkSZIkSRXH\nUEySJEmSJEkVx1BMkiRJkiRJFcdQTJIkSZIkSRXHUEySJEmSJEkVx1BMkiRJkiRJFcdQTJIkSZIk\nSRXHUEySJEmSJEkVx1BMkiRJkiRJFSflnMvdhwEtpdQCpFGjRpW7K1Kf1tzcDMCgQYPK3BOpb/Na\nkTrHa0XqHK8VqXO8VvqeNWvWAOSc8w4XfBmK9bCUUus/8JqydkTq+1qTY68Vadu8VqTO8VqROsdr\nReocr5W+ZyTQknOu3tET7PAT1Wmt0eXocndE6stSSqvBa0XaHq8VqXO8VqTO8VqROsdrZWByTjFJ\nkiRJkiRVHEMxSZIkSZIkVRxDMUmSJEmSJFUcQzFJkiRJkiRVHEMxSZIkSZIkVRxDMUmSJEmSJFUc\nQzFJkiRJkiRVnJRzLncfJEmSJEmSpF5lpZgkSZIkSZIqjqGYJEmSJEmSKo6hmCRJkiRJkiqOodgO\nSimdl1LKKaVDyt0Xqa8puj5ySun1JfYPSSmtLuy/vAxdlMrKa0R67Xb0s1hK6bjC847roa5JZZNS\nOiqldG1K6fmUUkNKaXlK6U8ppU+Xu29SX1H0/lGfUppUYv9DKaXbytA1lYGhmKSetA54f4ntZwF1\nQEPvdkfqc7xGJEndIqU0C/gzMAT4JPBm4J+BR4Bzytg1qa8aAvxruTuh8jIUk9ST/g94V0qprsP2\n9wPXAJu64y9JKVWnlAZ1x7mkXtYr14gkqSJ8CngSODXn/Ouc820551/mnP8B2KIqWRJzgHNTSlPL\n3RGVj6FYN0kpnZxSuialtLRQhrkwpfStlNLIDsddXhgSs39KaU5KaX1K6bmU0jdTSoPL1X+ph/wP\nUe1yVuuGlNLuwInA5cUHFoaLfSul9EhKaW1K6eVCuf+JHY6bXCh3/ueU0hdTSs8Bm4Hde/y3kbpf\nt14jKaWhKaWVKaUfdfyLUkoHFq6dD/TYbyOVUUrpmVLDjVNKtzkMRhViJ+ClnHNzxx0555bWP6eU\nqlJKn0gpPZpS2lR4P/l5Sml88XMK19RVKaV3pZQeKxz7eErpPb3wu0i94WvAGuAr2zoopbRLSum/\nUkovpJQ2p5QWpZQ+23pTPqVUk1J6KaV0WYnn7p5Sak4pXdQzv4JeK0Ox7rMPcAfwIeBU4OvAGcD1\nJY4dDFwN3Fw45jLgE4AXigaalUS1S/HwsHOBF4k7M8UGA6OBrwJvA94HPAb8PqV0UolzfwI4Ejgf\nOBNY1a09l3pHt14jOeeNwE+B93S8KUNcK6uAX3Xz7yBJ6hvuAY4u3Gw/PKVUvZXjLgP+jXj/eSsx\nxPJ44LaU0rAOxx5BBAZfAd4OLAKuSCm9oyd+AamXrSauhTNTSjNLHZBSGgrcBvwVcAkwi7h2vgz8\nGCDn3Ejc6Hx7iWvofUAGftED/Vc32NoLpboo5/zqXfmUUgLuBJ4A/pRSOjjn/HDR4UOAz+Scf1f4\n+Y8ppcOB9wBf7K0+S73kMuDalNIeOeclwHnAL3LOzXGphJzzGuDVCpbCnZebgb2JL/N/6HDedcCs\nUndDpX6mu6+R7xOh8fuA7xWOHUm8x/w451zf07+QJKksPkPcqP+nQqtPKd0FXEW8/jemlI4i3h/O\nzzn/oPWJKaWHgAeJ96DvF51zN2B6zvmxwnE3AI8CXyKmAJD6u+8BHyNuOh5bYv+5wDTgrTnn6wrb\nbk4pVQEfTyl9I+f8OPF57mNEePyzoue/D7g557ysp34BvTZWinWTlNL4lNJ3U0pPE3PANAJ/Kuw+\noMPhzWxZQfYIsGfP9lIqi5uIqpdzU0pvIj6sXV7qwJTSOSmlO1JKrwBNxHV0ElteQwDXGIhpgOjW\nayTn/DRwHfCRoqeeCwwDftgTv4Akqfxyzi/lnI8HDgMuBG4o/PlS4O6U0hDgLUAL8L+FOVmrCxVl\n84ClbBkKPNwaiBX+jhYiDNuv43BLqT/KOW8CPg+8KaV0eolDjgfWFAVirX5eeDyucJ6HgIeJYBmI\n1WCB/djK5zr1DYZi3aCQEv8eeCfwDeAUYljX2YVDOk6gvD7nvLnDtgZiaIw0oBSCq18QbxAfAO4t\n/nDVKqX0V8BvgeeIoWRvIK6jOWx5DQEs76EuS72qh66R7wLTUkrHFX7+CDAn5/xUD/wKkqQ+JOc8\nN+f87znntwMTiWFdhxPvMbsS3wFfIW6sFLdJwM4dTvdCib/ixcLjTt3fe6ksfgbMB/6t8N2+2FhK\nXwet30WKr4OfAcemlCYXfj6PmLri6u7qqLqfwye7x4HADODcnHNrYkxKaVT5uiT1KZcRdywnAx/d\nyjHvBhYD784559aNKaXhWzk+b2W71B916zWSc/5jSmkB8NHCh7upxKpk0kC2idI3GHcGXu7lvkh9\nQs65PqX0NeI9ZAZxLbQAxxBBWEfrOvxcqhps18LjK93VT6mccs4tKaXPEuFVx4UkXiHm1utoQtH+\nVlcQk/efW7ju3gH8Mufc0M1dVjcyFOserV9OOlZ//V1vd0Tqi3LOj6eUfkisEPnrrR0GNHb4sj8d\nmAk83/O9lMqnh66RS4mKsV2Bp4Ebu7XTUt/zDHBQ8YaU0n7A/hiKqQKklCbknEtV0k8tPC4Hbgc+\nDYwvmt94Ww5KKU0tmlOsiviivzDnXKp6RuqXcs7XpJTuJOb4Lp5/9RbgHSmlWR2GUP4N8dns1qJz\nvJRSupGYR2whMIq48ak+zFDstcvE6l+Lga8WJkVeA/w1UaYsCcg5b636pdX1wFkppe8DVxKTh38B\neBYY1LO9k8qvB66RXxArKr0JuLAwD4w0ELUGxVcAv0gp/QCYTczVeiHwUrk6JvWyOSml54iV8RYC\nNcDriNUlVwD/nXNenlK6HPh5SulS4A6iynIiMXfSDTnn3xadczlwXUrp88QwsA8Tk46/s3d+JalX\nXURcExDXDMTcYf9ArLr6L8DjxHRJnwD+K+f8RIdzXE58Tvs6MD/nfH9Pd1qvjaHYjhtaeGworOTy\nNuKO/H8R84NdC7wL8CKQOuenRIn+3wMfJN5wPkYsFX5c+bol9RldukZyzhtSStcRqyD9tPe6KfWa\nVz+LFR5/SQxn+TAx7948Yj69z/d+16SyuAQ4ixguP4EIxZYSE+NfUlRF9gHgHmJUy8eJ4ZRLiSqy\nRzqc837gf4nraG+iIvO9OWdXntSAk3O+M6V0DfC2om31KaXjiRuQUZaeAAAFHElEQVSNFxNzjD0L\nfJYIvjq6jrgZMwn4To93Wq9ZKhqFoS5IKX0HOB8YmXPeWO7+SJJUrLDK2LPEXf/3l7s/Unfzs5jU\ns1JKzwAP5ZzPLHdfJKmnWCnWRSmlw4nVvj4A/M4PYZKkviSltBOwL/AhYkWkb5S3R1L38rOYJEnq\nLoZiXfcb4t/tl8Q8FZIk9SVvJSZ1XQZ8KOc8v8z9kbqbn8UkSVK3cPikJEmSJEmSKk5VuTsgSZIk\nSZIk9TZDMUmSJEmSJFUcQzFJkiRJkiRVHEMxSZIkSZIkVRxDMUmSJEmSJFUcQzFJkiRJkiRVHEMx\nSZIkSZIkVRxDMUmSJEmSJFUcQzFJkiRJkiRVHEMxSZIkSZIkVRxDMUmSJEmSJFUcQzFJkiRJkiRV\nHEMxSZIkSZIkVRxDMUmSJEmSJFUcQzFJkiRJkiRVHEMxSZIkSZIkVRxDMUmSJEmSJFUcQzFJkiRJ\nkiRVHEMxSZIkSZIkVRxDMUmSJHW7lNIXUko5pTS53H2RJEkqxVBMkiSpH0gpHVcImXJK6XtbOWZc\nSmlz4ZjbeqlPX0gpje7pv0uSJKm7GYpJkiT1L5uAd6eUBpfY914gAU291JfjgM8DhmKSJKnfMRST\nJEnqX34HjAHOKLHv/cANQEOv9kiSJKkfMhSTJEnqXx4EHiYCsFellF4HTAcuK/WklNKZKaU7U0rr\nC+3OlNIWwVpK6ZmU0m0ppQNSStenlNallNaklH6bUhpfdNzlRJUYwNNFQzu/0OGUg1NKX0kpPZ9S\nakgpPZxSessO//aSJEndpLrcHZAkSVKXXQZ8K6U0Kef8fGHbB4AVwHUdD04pfRT4PvA48GUgA+cB\nV6WUPpRz/s8OT5kI3EZUpX0KOBj4EDASOKVwzI8LP58FfAJ4ubD9kQ7n+hnQCHwDqAU+Xvh798s5\nP9PF31uSJKnbGIpJkiT1P1cAXwfeB3wlpVQHvAv475xzU0rp1QNTSmMKxz4FvD7nvLaw/YfAXOCb\nKaX/yzmvLjr/FOCdOef/KzpPC/DRlNIBOefHc853p5QeIUKxq7YRcL0MvDXnnAvnuRX4CxGyfea1\n/kNIkiTtKIdPSpIk9TM551eAa4hqL4CzgVHAT0scfjIwDPhuayBWOMda4FJgOHBSh+csKw7ECm4p\nPE7pYne/0xqIFf7e+4B1wL5dPI8kSVK3MhSTJEnqny4D9k0pHUMMnfxLznlBieP2KjzOL7FvXuFx\n7w7bF5c49pXC405d7Gepc63cgfNIkiR1K0MxSZKk/ukmYCkx2f3xlK4SA0hb2b4tzdvY19Xzbe1c\nO9IvSZKkbmMoJkmS1A/lnJuBnxNDHzcB/7uVQ58qPE4vsW9a4bFUNVenurGDz5MkSSo7J9qXJEnq\nv34EbAYW55zXbOWY3wMbgAtSSpflnNcBpJRGABcA6wvH7Ij1hcexwDM7eA5JkqSyMBSTJEnqp3LO\nS4AvbOeY1SmlC4HvA/emlC4v7DqPmDT/Q9sI1LbnnsLj11JKvyQq1ublnOdt4zmSJEl9gsMnJUmS\nBric8w+IFSpXE3OQfb7w57Nyzv/5Gs57J3ARsA/wX8CvgLe/5g5LkiT1glS0QrYkSZIkSZJUEawU\nkyRJkiRJUsUxFJMkSZIkSVLFMRSTJEmSJElSxTEUkyRJkiRJUsUxFJMkSZIkSVLFMRSTJEmSJElS\nxTEUkyRJkiRJUsUxFJMkSZIkSVLFMRSTJEmSJElSxTEUkyRJkiRJUsX5/5hYvY+T5D/5AAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d043ec9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\")\n",
    "ax=df_age_T_2yrs.plot(kind='line', figsize=(20,12),x='MonthName', grid=True, style='o-')\n",
    "ax.yaxis.set_major_formatter(tick.FuncFormatter(fmt_x))\n",
    "plt.xlabel(\"Month\", labelpad=20)\n",
    "plt.ylabel(\"Days on Market\", labelpad=20)\n",
    "plt.title(\"Days on Market for Inventory - USA(whole) and SF in 2017\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis : Observing the same seasonality trend seen before by zooming in into a single year(2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>03</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>04</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>05</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year Month  United States\n",
       "0  2012    01            119\n",
       "1  2012    02            114\n",
       "2  2012    03            102\n",
       "3  2012    04             84\n",
       "4  2012    05             78"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DOM_US = df_age_T.ix[:, ['Year', 'Month','United States']]\n",
    "df_DOM_US.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>119</td>\n",
       "      <td>109</td>\n",
       "      <td>103</td>\n",
       "      <td>106</td>\n",
       "      <td>102</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>114</td>\n",
       "      <td>100</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>102</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>84</td>\n",
       "      <td>76</td>\n",
       "      <td>71</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      United States                         \n",
       "Year           2012 2013 2014 2015 2016 2017\n",
       "Month                                       \n",
       "01              119  109  103  106  102  104\n",
       "02              114  100  102  102   98  102\n",
       "03              102   86   90   86   72   86\n",
       "04               84   76   71   65   68   68\n",
       "05               78   74   66   60   64   62"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DOM_US_2017 = df_DOM_US[(df_DOM_US['Year'].astype(int) < 2018)]\n",
    "df_DOM_US_idx = df_DOM_US_2017.set_index(['Year','Month'])\n",
    "dfDOMbyYrMon = df_DOM_US_idx.unstack(level=0)\n",
    "dfDOMbyYrMon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "month_short_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec']\n",
    "year_short_names = ['2012', '2013', '2014', '2015', '2016', '2017']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Days On Market trend - Heat Map <a class=\"anchor\" id=\"plot7\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zx6PBMteETWuGv9Y6/IuQi4LtLVLORFlflyDdO4lsS6I/JW17MelOD9LNjZh+bDD9oxjp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\nkiRJklSw/x+bByMBVXgkegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d044738fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"poster\")\n",
    "f, ax = plt.subplots(figsize=(20,14))\n",
    "ax = sns.heatmap(dfDOMbyYrMon, annot=True, linewidths=.9, ax=ax,fmt=\"0.0f\", yticklabels=month_short_names, xticklabels=year_short_names, cmap=\"Purples\")\n",
    "ax.axes.set_title(\"Inventory Trend - Avg Days On Market by Month and Year (US) - (2012 - 2017)\", fontsize=20, y=1.01)\n",
    "ax.set(xlabel='Year', ylabel='Month');\n",
    "# This sets the yticks \"upright\" with 0, as opposed to sideways with 90.\n",
    "plt.yticks(rotation=0)\n",
    "ax.xaxis.labelpad = 25\n",
    "ax.yaxis.labelpad = 25\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insights : Usually May and June are the best months to Buy/sell houses, since both buyers and sellers are active during summer and houses tend to get sold faster."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}